diff --git a/Model Training/.ipynb_checkpoints/DNN_and_Visualization_with_hash-checkpoint.ipynb b/Model Training/.ipynb_checkpoints/DNN_and_Visualization_with_hash-checkpoint.ipynb index b4b854a..43f5e6d 100644 --- a/Model Training/.ipynb_checkpoints/DNN_and_Visualization_with_hash-checkpoint.ipynb +++ b/Model Training/.ipynb_checkpoints/DNN_and_Visualization_with_hash-checkpoint.ipynb @@ -80,12 +80,17 @@ "metadata": {}, "outputs": [], "source": [ - "X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", + "# covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", + "\n", + "X = pd.read_csv('../data/Training_Data/Training_Dataset_with_threshold.csv')\n", "repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", "covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", - "y = pd.read_csv('../data/Training_Data/Labels.csv')\n", - "repackaged_benign_test_y = pd.read_csv('../data/Test_Data/Labels_Repackaged_Benign_Test.csv')\n", - "COVID_test_y = pd.read_csv('../data/Test_Data/Labels_COVID_Test.csv')" + "\n", + "y = pd.read_csv('../data/Training_Data/Labels_trainingset.csv')\n", + "repackaged_benign_test_y = pd.read_csv('../data/Test_Data/Labels_testset.csv')\n", + "COVID_test_y = pd.read_csv('../data/Test_Data/Labels_COVID_testset.csv')" ] }, { @@ -102,7 +107,7 @@ " 'Family_Name', 'Malware_Category', 'Malware/Benign','sdkVersion', 'targetSdkVersion'], axis = 1, inplace = True)\n", "\n", "covid_test_X.drop(['Package_Name', 'SHA256', 'ThumbPrint', 'Application_Category', \n", - " 'Family_Name', 'Malware_Category', 'Malware/Benign','sdkVersion', 'targetSdkVersion'], axis = 1, inplace = True)\n" + " 'Family_Name', 'Malware_Category', 'Malware/Benign','sdkVersion', 'targetSdkVersion'], axis = 1, inplace = True)" ] }, { @@ -121,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -141,7 +146,18 @@ { "data": { "text/plain": [ - "Series([], Name: count, dtype: float64)" + "Permission: FACTORY_TEST 9538.0\n", + "Permission: DUMP 9538.0\n", + "Permission: BATTERY_STATS 9538.0\n", + "Permission: BIND_WALLPAPER 9538.0\n", + "Permission: BIND_INPUT_METHOD 9538.0\n", + " ... \n", + "rotation_vector 9538.0\n", + "temperature 9538.0\n", + "if_the_app_using_suspicious_libs 9538.0\n", + "malwareNum 9538.0\n", + "proportion 9538.0\n", + "Name: count, Length: 335, dtype: float64" ] }, "execution_count": 5, @@ -163,29 +179,23 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "X['label']=y['label']" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ + "X['label']=y['label']\n", "# Create dataset without sensor features in order to see if there's some improvement by adding sensor features\n", - "sensor_lst=list(X.iloc[:,-32:-3].columns)\n", + "sensor_lst=list(X.iloc[:,-14:-3].columns)\n", + "# sensor_lst.remove('if_the_app_using_suspicious_libs')\n", "X_wo_sensors = X.drop(sensor_lst, axis = 1)\n", + "\n", "repackaged_benign_test_X_wo_sensors = repackaged_benign_test_X.drop(sensor_lst, axis = 1)\n", "covid_test_X_wo_sensors = covid_test_X.drop(sensor_lst, axis = 1)\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -389,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -421,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -447,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -458,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -488,7 +498,7 @@ { "data": { "text/html": [ - " |-Trial ID: 46f49e42b566c459ef09f302d416834a" + " |-Trial ID: 627032c51d3b17641810230698085038" ], "text/plain": [ "" @@ -500,7 +510,7 @@ { "data": { "text/html": [ - " |-Score: 1.0" + " |-Score: 0.9960784316062927" ], "text/plain": [ "" @@ -536,7 +546,7 @@ { "data": { "text/html": [ - " |-learning_rate: 0.0001" + " |-learning_rate: 0.01" ], "text/plain": [ "" @@ -626,14 +636,14 @@ } ], "source": [ - "tuner.search(X_train_resample.drop(['proportion','label'],axis=1).values, y_train_resample.values, epochs = 20, \n", - " validation_data=(X_test.drop(['proportion','label'],axis=1).values, y_test.values),verbose=2, \n", + "tuner.search(X_train_resample.drop(['malwareNum','proportion','label'],axis=1).values, y_train_resample.values, epochs = 20, \n", + " validation_data=(X_test.drop(['malwareNum','proportion','label'],axis=1).values, y_test.values),verbose=2, \n", " callbacks = [ClearTrainingOutput()])" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -643,9 +653,9 @@ "\n", "The hyperparameter search is complete. \n", "\n", - "The optimal number of units in the first densely-connected layer is 64 \n", + "The optimal number of units in the first densely-connected layer is 32 \n", "\n", - "The optimal number of units in the second densely-connected layer is 16 \n", + "The optimal number of units in the second densely-connected layer is 48 \n", "\n", "The optimal learning rate for the optimizer\n", "is 0.0001.\n", @@ -662,14 +672,12 @@ "The optimal number of units in the second densely-connected layer is {best_hps.get('units2')} \\n\n", "The optimal learning rate for the optimizer\n", "is {best_hps.get('learning_rate')}.\n", - "\"\"\")\n", - "\n", - "\n" + "\"\"\")" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -699,7 +707,7 @@ { "data": { "text/html": [ - " |-Trial ID: 17be068b8be392e95a4a9d7ad288c694" + " |-Trial ID: 86fec3cd61894a8f98d8cf5f126d9a53" ], "text/plain": [ "" @@ -747,7 +755,7 @@ { "data": { "text/html": [ - " |-learning_rate: 0.01" + " |-learning_rate: 0.0001" ], "text/plain": [ "" @@ -819,7 +827,7 @@ { "data": { "text/html": [ - " |-units2: 16" + " |-units2: 48" ], "text/plain": [ "" @@ -855,14 +863,14 @@ "# project_name='2_layers',\n", "# overwrite = True) \n", "\n", - "tuner.search(X_train_wo_sensor_resample.drop(['proportion','label'],axis=1).values,y_train_wo_sensor_resample.values, epochs = 20,\n", + "tuner.search(X_train_wo_sensor_resample.drop(['malwareNum','proportion','label'],axis=1).values,y_train_wo_sensor_resample.values, epochs = 20,\n", " validation_split=0.2,verbose=2,\n", " callbacks = [ClearTrainingOutput()])" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -872,9 +880,9 @@ "\n", "The hyperparameter search is complete. \n", "\n", - "The optimal number of units in the first densely-connected layer is 48 \n", + "The optimal number of units in the first densely-connected layer is 64 \n", "\n", - "The optimal number of units in the second densely-connected layer is 64 \n", + "The optimal number of units in the second densely-connected layer is 48 \n", "\n", "The optimal learning rate for the optimizer\n", "is 0.0001.\n", @@ -903,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1022,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1033,18 +1041,18 @@ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_1 (Dense) (None, 64) 22656 \n", + "dense_1 (Dense) (None, 32) 10688 \n", "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 64) 0 \n", + "dropout_1 (Dropout) (None, 32) 0 \n", "_________________________________________________________________\n", - "dense_2 (Dense) (None, 16) 1040 \n", + "dense_2 (Dense) (None, 48) 1584 \n", "_________________________________________________________________\n", - "dropout_2 (Dropout) (None, 16) 0 \n", + "dropout_2 (Dropout) (None, 48) 0 \n", "_________________________________________________________________\n", - "dense_3 (Dense) (None, 1) 17 \n", + "dense_3 (Dense) (None, 1) 49 \n", "=================================================================\n", - "Total params: 23,713\n", - "Trainable params: 23,713\n", + "Total params: 12,321\n", + "Trainable params: 12,321\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -1054,9 +1062,9 @@ "tf.keras.backend.clear_session()\n", "model = keras.Sequential()\n", "model = Sequential()\n", - "model.add(Dense(64, input_dim=X_train.shape[1]-2, activation='relu'))\n", + "model.add(Dense(32, input_dim=X_train_resample.drop(['malwareNum','proportion','label'],axis=1).shape[1], activation='relu'))\n", "model.add(Dropout(0.4))\n", - "model.add(Dense(16, activation='relu'))\n", + "model.add(Dense(48, activation='relu'))\n", "model.add(Dropout(0.4))\n", "# model.add(Dense(128, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", @@ -1068,221 +1076,221 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train on 13162 samples, validate on 2230 samples\n", + "Train on 10848 samples, validate on 1908 samples\n", "Epoch 1/100\n", - " - 5s - loss: 0.6362 - accuracy: 0.6255 - auc: 0.6253 - val_loss: 0.5429 - val_accuracy: 0.7184 - val_auc: 0.6920\n", + " - 3s - loss: 0.6530 - accuracy: 0.6037 - auc: 0.5965 - val_loss: 0.5537 - val_accuracy: 0.8234 - val_auc: 0.6774\n", "Epoch 2/100\n", - " - 2s - loss: 0.5346 - accuracy: 0.7361 - auc: 0.7344 - val_loss: 0.4583 - val_accuracy: 0.7996 - val_auc: 0.7644\n", + " - 2s - loss: 0.5556 - accuracy: 0.7241 - auc: 0.7233 - val_loss: 0.4616 - val_accuracy: 0.8103 - val_auc: 0.7589\n", "Epoch 3/100\n", - " - 2s - loss: 0.4839 - accuracy: 0.7731 - auc: 0.7847 - val_loss: 0.4369 - val_accuracy: 0.7919 - val_auc: 0.8004\n", + " - 2s - loss: 0.4785 - accuracy: 0.7798 - auc: 0.7830 - val_loss: 0.4054 - val_accuracy: 0.8155 - val_auc: 0.8040\n", "Epoch 4/100\n", - " - 3s - loss: 0.4494 - accuracy: 0.7969 - auc: 0.8127 - val_loss: 0.3906 - val_accuracy: 0.8027 - val_auc: 0.8233\n", + " - 2s - loss: 0.4248 - accuracy: 0.8107 - auc: 0.8192 - val_loss: 0.3754 - val_accuracy: 0.8187 - val_auc: 0.8315\n", "Epoch 5/100\n", - " - 3s - loss: 0.4218 - accuracy: 0.8107 - auc: 0.8322 - val_loss: 0.3742 - val_accuracy: 0.8076 - val_auc: 0.8396\n", + " - 2s - loss: 0.3888 - accuracy: 0.8249 - auc: 0.8413 - val_loss: 0.3523 - val_accuracy: 0.8333 - val_auc: 0.8503\n", "Epoch 6/100\n", - " - 2s - loss: 0.4006 - accuracy: 0.8195 - auc: 0.8458 - val_loss: 0.3745 - val_accuracy: 0.8090 - val_auc: 0.8518\n", + " - 2s - loss: 0.3617 - accuracy: 0.8403 - auc: 0.8577 - val_loss: 0.3409 - val_accuracy: 0.8386 - val_auc: 0.8643\n", "Epoch 7/100\n", - " - 3s - loss: 0.3887 - accuracy: 0.8264 - auc: 0.8566 - val_loss: 0.3765 - val_accuracy: 0.8000 - val_auc: 0.8608\n", + " - 2s - loss: 0.3506 - accuracy: 0.8442 - auc: 0.8700 - val_loss: 0.3132 - val_accuracy: 0.8454 - val_auc: 0.8748\n", "Epoch 8/100\n", - " - 2s - loss: 0.3743 - accuracy: 0.8309 - auc: 0.8645 - val_loss: 0.3516 - val_accuracy: 0.8211 - val_auc: 0.8683\n", + " - 2s - loss: 0.3314 - accuracy: 0.8520 - auc: 0.8794 - val_loss: 0.3171 - val_accuracy: 0.8375 - val_auc: 0.8834\n", "Epoch 9/100\n", - " - 3s - loss: 0.3623 - accuracy: 0.8364 - auc: 0.8715 - val_loss: 0.3452 - val_accuracy: 0.8184 - val_auc: 0.8747\n", + " - 2s - loss: 0.3162 - accuracy: 0.8616 - auc: 0.8873 - val_loss: 0.3079 - val_accuracy: 0.8401 - val_auc: 0.8908\n", "Epoch 10/100\n", - " - 2s - loss: 0.3556 - accuracy: 0.8426 - auc: 0.8775 - val_loss: 0.3587 - val_accuracy: 0.8108 - val_auc: 0.8801\n", + " - 2s - loss: 0.3070 - accuracy: 0.8661 - auc: 0.8940 - val_loss: 0.2913 - val_accuracy: 0.8627 - val_auc: 0.8970\n", "Epoch 11/100\n", - " - 3s - loss: 0.3406 - accuracy: 0.8504 - auc: 0.8826 - val_loss: 0.3294 - val_accuracy: 0.8390 - val_auc: 0.8851\n", + " - 2s - loss: 0.2988 - accuracy: 0.8658 - auc: 0.8997 - val_loss: 0.2769 - val_accuracy: 0.8732 - val_auc: 0.9023\n", "Epoch 12/100\n", - " - 3s - loss: 0.3364 - accuracy: 0.8528 - auc: 0.8873 - val_loss: 0.3262 - val_accuracy: 0.8395 - val_auc: 0.8895\n", + " - 2s - loss: 0.2937 - accuracy: 0.8741 - auc: 0.9045 - val_loss: 0.2846 - val_accuracy: 0.8622 - val_auc: 0.9068\n", "Epoch 13/100\n", - " - 2s - loss: 0.3320 - accuracy: 0.8555 - auc: 0.8914 - val_loss: 0.3184 - val_accuracy: 0.8426 - val_auc: 0.8932\n", + " - 2s - loss: 0.2811 - accuracy: 0.8787 - auc: 0.9088 - val_loss: 0.2811 - val_accuracy: 0.8632 - val_auc: 0.9109\n", "Epoch 14/100\n", - " - 2s - loss: 0.3218 - accuracy: 0.8596 - auc: 0.8950 - val_loss: 0.3109 - val_accuracy: 0.8448 - val_auc: 0.8967\n", + " - 2s - loss: 0.2767 - accuracy: 0.8794 - auc: 0.9127 - val_loss: 0.2849 - val_accuracy: 0.8632 - val_auc: 0.9144\n", "Epoch 15/100\n", - " - 2s - loss: 0.3156 - accuracy: 0.8651 - auc: 0.8984 - val_loss: 0.3109 - val_accuracy: 0.8453 - val_auc: 0.8999\n", + " - 2s - loss: 0.2715 - accuracy: 0.8873 - auc: 0.9160 - val_loss: 0.2661 - val_accuracy: 0.8648 - val_auc: 0.9176\n", "Epoch 16/100\n", - " - 2s - loss: 0.3095 - accuracy: 0.8680 - auc: 0.9014 - val_loss: 0.3184 - val_accuracy: 0.8466 - val_auc: 0.9028\n", + " - 2s - loss: 0.2662 - accuracy: 0.8877 - auc: 0.9191 - val_loss: 0.2669 - val_accuracy: 0.8679 - val_auc: 0.9205\n", "Epoch 17/100\n", - " - 2s - loss: 0.3059 - accuracy: 0.8682 - auc: 0.9042 - val_loss: 0.3146 - val_accuracy: 0.8457 - val_auc: 0.9055\n", + " - 2s - loss: 0.2597 - accuracy: 0.8900 - auc: 0.9218 - val_loss: 0.2708 - val_accuracy: 0.8700 - val_auc: 0.9231\n", "Epoch 18/100\n", - " - 2s - loss: 0.2981 - accuracy: 0.8738 - auc: 0.9067 - val_loss: 0.3122 - val_accuracy: 0.8453 - val_auc: 0.9079\n", + " - 2s - loss: 0.2530 - accuracy: 0.8945 - auc: 0.9244 - val_loss: 0.2571 - val_accuracy: 0.8753 - val_auc: 0.9256\n", "Epoch 19/100\n", - " - 2s - loss: 0.2969 - accuracy: 0.8746 - auc: 0.9091 - val_loss: 0.3121 - val_accuracy: 0.8507 - val_auc: 0.9102\n", + " - 2s - loss: 0.2475 - accuracy: 0.8961 - auc: 0.9268 - val_loss: 0.2653 - val_accuracy: 0.8700 - val_auc: 0.9279\n", "Epoch 20/100\n", - " - 2s - loss: 0.2950 - accuracy: 0.8775 - auc: 0.9112 - val_loss: 0.3077 - val_accuracy: 0.8534 - val_auc: 0.9122\n", + " - 2s - loss: 0.2453 - accuracy: 0.8934 - auc: 0.9289 - val_loss: 0.2624 - val_accuracy: 0.8711 - val_auc: 0.9299\n", "Epoch 21/100\n", - " - 2s - loss: 0.2866 - accuracy: 0.8796 - auc: 0.9132 - val_loss: 0.3133 - val_accuracy: 0.8511 - val_auc: 0.9141\n", + " - 2s - loss: 0.2435 - accuracy: 0.8968 - auc: 0.9308 - val_loss: 0.2536 - val_accuracy: 0.8800 - val_auc: 0.9317\n", "Epoch 22/100\n", - " - 2s - loss: 0.2825 - accuracy: 0.8828 - auc: 0.9150 - val_loss: 0.3007 - val_accuracy: 0.8610 - val_auc: 0.9159\n", + " - 2s - loss: 0.2379 - accuracy: 0.8988 - auc: 0.9326 - val_loss: 0.2557 - val_accuracy: 0.8789 - val_auc: 0.9335\n", "Epoch 23/100\n", - " - 3s - loss: 0.2823 - accuracy: 0.8838 - auc: 0.9168 - val_loss: 0.3061 - val_accuracy: 0.8570 - val_auc: 0.9176\n", + " - 2s - loss: 0.2353 - accuracy: 0.9001 - auc: 0.9342 - val_loss: 0.2605 - val_accuracy: 0.8753 - val_auc: 0.9350\n", "Epoch 24/100\n", - " - 4s - loss: 0.2759 - accuracy: 0.8861 - auc: 0.9184 - val_loss: 0.3108 - val_accuracy: 0.8592 - val_auc: 0.9192\n", + " - 2s - loss: 0.2314 - accuracy: 0.9028 - auc: 0.9358 - val_loss: 0.2479 - val_accuracy: 0.8784 - val_auc: 0.9366\n", "Epoch 25/100\n", - " - 3s - loss: 0.2761 - accuracy: 0.8852 - auc: 0.9199 - val_loss: 0.2999 - val_accuracy: 0.8610 - val_auc: 0.9207\n", + " - 2s - loss: 0.2279 - accuracy: 0.9050 - auc: 0.9373 - val_loss: 0.2529 - val_accuracy: 0.8768 - val_auc: 0.9380\n", "Epoch 26/100\n", - " - 3s - loss: 0.2738 - accuracy: 0.8888 - auc: 0.9214 - val_loss: 0.3049 - val_accuracy: 0.8601 - val_auc: 0.9220\n", + " - 2s - loss: 0.2245 - accuracy: 0.9062 - auc: 0.9387 - val_loss: 0.2465 - val_accuracy: 0.8810 - val_auc: 0.9394\n", "Epoch 27/100\n", - " - 3s - loss: 0.2680 - accuracy: 0.8917 - auc: 0.9227 - val_loss: 0.3062 - val_accuracy: 0.8592 - val_auc: 0.9234\n", + " - 2s - loss: 0.2236 - accuracy: 0.9070 - auc: 0.9400 - val_loss: 0.2465 - val_accuracy: 0.8826 - val_auc: 0.9406\n", "Epoch 28/100\n", - " - 3s - loss: 0.2638 - accuracy: 0.8929 - auc: 0.9240 - val_loss: 0.2926 - val_accuracy: 0.8664 - val_auc: 0.9247\n", + " - 2s - loss: 0.2200 - accuracy: 0.9101 - auc: 0.9412 - val_loss: 0.2462 - val_accuracy: 0.8868 - val_auc: 0.9418\n", "Epoch 29/100\n", - " - 3s - loss: 0.2613 - accuracy: 0.8939 - auc: 0.9252 - val_loss: 0.3028 - val_accuracy: 0.8605 - val_auc: 0.9259\n", + " - 2s - loss: 0.2202 - accuracy: 0.9065 - auc: 0.9423 - val_loss: 0.2494 - val_accuracy: 0.8857 - val_auc: 0.9429\n", "Epoch 30/100\n", - " - 3s - loss: 0.2615 - accuracy: 0.8938 - auc: 0.9264 - val_loss: 0.3028 - val_accuracy: 0.8601 - val_auc: 0.9270\n", + " - 2s - loss: 0.2135 - accuracy: 0.9117 - auc: 0.9434 - val_loss: 0.2519 - val_accuracy: 0.8831 - val_auc: 0.9439\n", "Epoch 31/100\n", - " - 3s - loss: 0.2572 - accuracy: 0.8963 - auc: 0.9275 - val_loss: 0.3014 - val_accuracy: 0.8596 - val_auc: 0.9281\n", + " - 2s - loss: 0.2127 - accuracy: 0.9153 - auc: 0.9445 - val_loss: 0.2403 - val_accuracy: 0.8831 - val_auc: 0.9450\n", "Epoch 32/100\n", - " - 2s - loss: 0.2602 - accuracy: 0.8939 - auc: 0.9286 - val_loss: 0.3041 - val_accuracy: 0.8587 - val_auc: 0.9290\n", + " - 2s - loss: 0.2129 - accuracy: 0.9121 - auc: 0.9454 - val_loss: 0.2435 - val_accuracy: 0.8868 - val_auc: 0.9459\n", "Epoch 33/100\n", - " - 3s - loss: 0.2546 - accuracy: 0.8976 - auc: 0.9295 - val_loss: 0.3036 - val_accuracy: 0.8596 - val_auc: 0.9300\n", + " - 2s - loss: 0.2072 - accuracy: 0.9143 - auc: 0.9464 - val_loss: 0.2390 - val_accuracy: 0.8863 - val_auc: 0.9468\n", "Epoch 34/100\n", - " - 3s - loss: 0.2480 - accuracy: 0.9001 - auc: 0.9305 - val_loss: 0.2964 - val_accuracy: 0.8623 - val_auc: 0.9310\n", + " - 2s - loss: 0.2093 - accuracy: 0.9143 - auc: 0.9472 - val_loss: 0.2424 - val_accuracy: 0.8868 - val_auc: 0.9477\n", "Epoch 35/100\n", - " - 2s - loss: 0.2495 - accuracy: 0.8972 - auc: 0.9314 - val_loss: 0.3014 - val_accuracy: 0.8605 - val_auc: 0.9319\n", + " - 2s - loss: 0.2057 - accuracy: 0.9172 - auc: 0.9481 - val_loss: 0.2403 - val_accuracy: 0.8873 - val_auc: 0.9485\n", "Epoch 36/100\n", - " - 3s - loss: 0.2475 - accuracy: 0.9021 - auc: 0.9323 - val_loss: 0.3055 - val_accuracy: 0.8587 - val_auc: 0.9327\n", + " - 2s - loss: 0.2011 - accuracy: 0.9159 - auc: 0.9489 - val_loss: 0.2406 - val_accuracy: 0.8878 - val_auc: 0.9493\n", "Epoch 37/100\n", - " - 3s - loss: 0.2451 - accuracy: 0.9037 - auc: 0.9331 - val_loss: 0.2986 - val_accuracy: 0.8641 - val_auc: 0.9335\n", + " - 2s - loss: 0.1980 - accuracy: 0.9178 - auc: 0.9497 - val_loss: 0.2405 - val_accuracy: 0.8863 - val_auc: 0.9501\n", "Epoch 38/100\n", - " - 3s - loss: 0.2453 - accuracy: 0.9021 - auc: 0.9339 - val_loss: 0.3073 - val_accuracy: 0.8570 - val_auc: 0.9343\n", + " - 2s - loss: 0.1983 - accuracy: 0.9194 - auc: 0.9504 - val_loss: 0.2406 - val_accuracy: 0.8857 - val_auc: 0.9508\n", "Epoch 39/100\n", - " - 3s - loss: 0.2411 - accuracy: 0.9054 - auc: 0.9347 - 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0.2373 - accuracy: 0.9046 - auc: 0.9375 - val_loss: 0.3068 - val_accuracy: 0.8605 - val_auc: 0.9378\n", + " - 2s - loss: 0.1933 - accuracy: 0.9218 - auc: 0.9537 - val_loss: 0.2349 - val_accuracy: 0.8899 - val_auc: 0.9540\n", "Epoch 44/100\n", - " - 2s - loss: 0.2331 - accuracy: 0.9089 - auc: 0.9381 - val_loss: 0.2996 - val_accuracy: 0.8677 - val_auc: 0.9385\n", + " - 2s - loss: 0.1942 - accuracy: 0.9208 - auc: 0.9543 - val_loss: 0.2366 - val_accuracy: 0.8905 - val_auc: 0.9545\n", "Epoch 45/100\n", - " - 2s - loss: 0.2338 - accuracy: 0.9067 - auc: 0.9388 - val_loss: 0.3063 - val_accuracy: 0.8632 - val_auc: 0.9390\n", + " - 2s - loss: 0.1874 - accuracy: 0.9241 - auc: 0.9548 - val_loss: 0.2356 - val_accuracy: 0.8899 - val_auc: 0.9551\n", "Epoch 46/100\n", - " - 3s - loss: 0.2315 - accuracy: 0.9086 - auc: 0.9393 - val_loss: 0.3029 - val_accuracy: 0.8650 - val_auc: 0.9396\n", + " - 2s - loss: 0.1890 - accuracy: 0.9233 - auc: 0.9554 - val_loss: 0.2357 - val_accuracy: 0.8894 - val_auc: 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val_auc: 0.9454\n", + " - 2s - loss: 0.1751 - accuracy: 0.9260 - auc: 0.9606 - val_loss: 0.2352 - val_accuracy: 0.8920 - val_auc: 0.9608\n", "Epoch 59/100\n", - " - 2s - loss: 0.2133 - accuracy: 0.9141 - auc: 0.9456 - val_loss: 0.3021 - val_accuracy: 0.8704 - val_auc: 0.9458\n", + " - 2s - loss: 0.1749 - accuracy: 0.9292 - auc: 0.9610 - val_loss: 0.2351 - val_accuracy: 0.8957 - val_auc: 0.9612\n", "Epoch 60/100\n", - " - 2s - loss: 0.2188 - accuracy: 0.9128 - auc: 0.9460 - val_loss: 0.3002 - val_accuracy: 0.8722 - val_auc: 0.9462\n", + " - 2s - loss: 0.1714 - accuracy: 0.9302 - auc: 0.9613 - val_loss: 0.2378 - val_accuracy: 0.8952 - val_auc: 0.9615\n", "Epoch 61/100\n", - " - 2s - loss: 0.2128 - accuracy: 0.9150 - auc: 0.9464 - val_loss: 0.3107 - val_accuracy: 0.8695 - val_auc: 0.9466\n", + " - 2s - loss: 0.1710 - accuracy: 0.9292 - auc: 0.9617 - val_loss: 0.2363 - val_accuracy: 0.8947 - val_auc: 0.9618\n", "Epoch 62/100\n", - " - 3s - loss: 0.2138 - accuracy: 0.9158 - auc: 0.9468 - 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0.2103 - accuracy: 0.9149 - auc: 0.9482 - val_loss: 0.3113 - val_accuracy: 0.8691 - val_auc: 0.9484\n", + " - 2s - loss: 0.1680 - accuracy: 0.9297 - auc: 0.9632 - val_loss: 0.2404 - val_accuracy: 0.8931 - val_auc: 0.9634\n", "Epoch 67/100\n", - " - 3s - loss: 0.2091 - accuracy: 0.9175 - auc: 0.9485 - val_loss: 0.3032 - val_accuracy: 0.8713 - val_auc: 0.9487\n", + " - 2s - loss: 0.1660 - accuracy: 0.9331 - auc: 0.9635 - val_loss: 0.2399 - val_accuracy: 0.8957 - val_auc: 0.9637\n", "Epoch 68/100\n", - " - 3s - loss: 0.2067 - accuracy: 0.9170 - auc: 0.9488 - val_loss: 0.3075 - val_accuracy: 0.8722 - val_auc: 0.9490\n", + " - 2s - loss: 0.1663 - accuracy: 0.9322 - auc: 0.9638 - val_loss: 0.2345 - val_accuracy: 0.8962 - val_auc: 0.9640\n", "Epoch 69/100\n", - " - 3s - loss: 0.2092 - accuracy: 0.9150 - auc: 0.9492 - val_loss: 0.3118 - val_accuracy: 0.8677 - val_auc: 0.9493\n", + " - 2s - loss: 0.1686 - accuracy: 0.9318 - auc: 0.9641 - val_loss: 0.2354 - val_accuracy: 0.9020 - val_auc: 0.9642\n", "Epoch 70/100\n", - " - 3s - loss: 0.2071 - accuracy: 0.9178 - auc: 0.9495 - val_loss: 0.3073 - val_accuracy: 0.8709 - val_auc: 0.9496\n", + " - 2s - loss: 0.1645 - accuracy: 0.9330 - auc: 0.9643 - val_loss: 0.2380 - val_accuracy: 0.8999 - val_auc: 0.9645\n", "Epoch 71/100\n", - " - 2s - loss: 0.2078 - accuracy: 0.9160 - auc: 0.9498 - val_loss: 0.3141 - val_accuracy: 0.8709 - val_auc: 0.9499\n", + " - 2s - loss: 0.1648 - accuracy: 0.9316 - auc: 0.9646 - val_loss: 0.2418 - val_accuracy: 0.9004 - val_auc: 0.9647\n", "Epoch 72/100\n", - " - 2s - loss: 0.2072 - accuracy: 0.9175 - auc: 0.9501 - val_loss: 0.3099 - val_accuracy: 0.8695 - val_auc: 0.9502\n", + " - 2s - loss: 0.1638 - accuracy: 0.9357 - auc: 0.9649 - val_loss: 0.2375 - val_accuracy: 0.8988 - val_auc: 0.9650\n", "Epoch 73/100\n", - " - 2s - loss: 0.2020 - accuracy: 0.9202 - auc: 0.9504 - val_loss: 0.3110 - val_accuracy: 0.8691 - val_auc: 0.9505\n", + " - 2s - loss: 0.1658 - accuracy: 0.9306 - auc: 0.9651 - val_loss: 0.2387 - val_accuracy: 0.8994 - val_auc: 0.9652\n", "Epoch 74/100\n", - " - 2s - loss: 0.2042 - accuracy: 0.9200 - auc: 0.9506 - val_loss: 0.3103 - val_accuracy: 0.8709 - val_auc: 0.9508\n", + " - 2s - loss: 0.1642 - accuracy: 0.9321 - auc: 0.9654 - val_loss: 0.2396 - val_accuracy: 0.9030 - val_auc: 0.9655\n", "Epoch 75/100\n", - " - 2s - loss: 0.2035 - accuracy: 0.9193 - auc: 0.9509 - val_loss: 0.3167 - val_accuracy: 0.8704 - val_auc: 0.9511\n", + " - 2s - loss: 0.1626 - accuracy: 0.9331 - auc: 0.9656 - val_loss: 0.2416 - val_accuracy: 0.9046 - val_auc: 0.9657\n", "Epoch 76/100\n", - " - 2s - loss: 0.1999 - accuracy: 0.9212 - auc: 0.9512 - val_loss: 0.3184 - val_accuracy: 0.8682 - val_auc: 0.9513\n", + " - 2s - loss: 0.1615 - accuracy: 0.9339 - auc: 0.9658 - val_loss: 0.2432 - val_accuracy: 0.9030 - val_auc: 0.9659\n", "Epoch 77/100\n", - " - 2s - loss: 0.1990 - accuracy: 0.9212 - auc: 0.9515 - val_loss: 0.3175 - val_accuracy: 0.8709 - val_auc: 0.9516\n", + " - 2s - loss: 0.1596 - 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val_auc: 0.9526\n", + " - 2s - loss: 0.1607 - accuracy: 0.9350 - auc: 0.9669 - val_loss: 0.2422 - val_accuracy: 0.9015 - val_auc: 0.9670\n", "Epoch 82/100\n", - " - 2s - loss: 0.1931 - accuracy: 0.9233 - auc: 0.9527 - val_loss: 0.3133 - val_accuracy: 0.8709 - val_auc: 0.9529\n", + " - 2s - loss: 0.1560 - accuracy: 0.9379 - auc: 0.9671 - val_loss: 0.2391 - val_accuracy: 0.9004 - val_auc: 0.9672\n", "Epoch 83/100\n", - " - 2s - loss: 0.1995 - accuracy: 0.9183 - auc: 0.9530 - val_loss: 0.3201 - val_accuracy: 0.8677 - val_auc: 0.9531\n", + " - 2s - loss: 0.1651 - accuracy: 0.9343 - auc: 0.9673 - val_loss: 0.2471 - val_accuracy: 0.9004 - val_auc: 0.9674\n", "Epoch 84/100\n", - " - 2s - loss: 0.1989 - accuracy: 0.9191 - auc: 0.9532 - val_loss: 0.3184 - val_accuracy: 0.8709 - val_auc: 0.9533\n", + " - 2s - loss: 0.1547 - accuracy: 0.9362 - auc: 0.9675 - val_loss: 0.2436 - val_accuracy: 0.9004 - val_auc: 0.9676\n", "Epoch 85/100\n", - " - 2s - loss: 0.1952 - accuracy: 0.9217 - auc: 0.9534 - val_loss: 0.3285 - val_accuracy: 0.8695 - val_auc: 0.9536\n", + " - 2s - loss: 0.1612 - accuracy: 0.9319 - auc: 0.9677 - val_loss: 0.2438 - val_accuracy: 0.9025 - val_auc: 0.9678\n", "Epoch 86/100\n", - " - 2s - loss: 0.1935 - accuracy: 0.9226 - auc: 0.9537 - val_loss: 0.3221 - val_accuracy: 0.8713 - val_auc: 0.9538\n", + " - 2s - loss: 0.1562 - accuracy: 0.9369 - auc: 0.9679 - val_loss: 0.2449 - val_accuracy: 0.9025 - val_auc: 0.9680\n", "Epoch 87/100\n", - " - 3s - loss: 0.1947 - accuracy: 0.9235 - auc: 0.9539 - val_loss: 0.3268 - val_accuracy: 0.8691 - val_auc: 0.9540\n", + " - 2s - loss: 0.1535 - accuracy: 0.9373 - auc: 0.9681 - val_loss: 0.2450 - val_accuracy: 0.9030 - val_auc: 0.9682\n", "Epoch 88/100\n", - " - 3s - loss: 0.1958 - accuracy: 0.9193 - auc: 0.9541 - val_loss: 0.3186 - val_accuracy: 0.8709 - val_auc: 0.9542\n", + " - 2s - loss: 0.1545 - accuracy: 0.9363 - auc: 0.9683 - val_loss: 0.2450 - val_accuracy: 0.9025 - val_auc: 0.9683\n", "Epoch 89/100\n", - " - 2s - loss: 0.1949 - accuracy: 0.9208 - auc: 0.9543 - val_loss: 0.3221 - val_accuracy: 0.8717 - val_auc: 0.9544\n", + " - 2s - loss: 0.1538 - accuracy: 0.9350 - auc: 0.9684 - val_loss: 0.2493 - val_accuracy: 0.9009 - val_auc: 0.9685\n", "Epoch 90/100\n", - " - 2s - loss: 0.1939 - accuracy: 0.9233 - auc: 0.9545 - val_loss: 0.3232 - val_accuracy: 0.8695 - val_auc: 0.9546\n", + " - 2s - loss: 0.1517 - accuracy: 0.9387 - auc: 0.9686 - val_loss: 0.2472 - val_accuracy: 0.9030 - val_auc: 0.9687\n", "Epoch 91/100\n", - " - 2s - loss: 0.1926 - accuracy: 0.9217 - auc: 0.9547 - val_loss: 0.3292 - val_accuracy: 0.8709 - val_auc: 0.9548\n", + " - 2s - loss: 0.1527 - accuracy: 0.9364 - auc: 0.9688 - val_loss: 0.2475 - val_accuracy: 0.9020 - val_auc: 0.9689\n", "Epoch 92/100\n", - " - 2s - loss: 0.1909 - accuracy: 0.9224 - auc: 0.9549 - val_loss: 0.3262 - val_accuracy: 0.8664 - val_auc: 0.9550\n", + " - 2s - loss: 0.1518 - accuracy: 0.9403 - auc: 0.9690 - val_loss: 0.2488 - val_accuracy: 0.9015 - val_auc: 0.9690\n", "Epoch 93/100\n", - " - 2s - loss: 0.1905 - accuracy: 0.9215 - auc: 0.9551 - val_loss: 0.3251 - val_accuracy: 0.8664 - val_auc: 0.9552\n", + " - 2s - loss: 0.1510 - accuracy: 0.9375 - auc: 0.9691 - val_loss: 0.2485 - val_accuracy: 0.8999 - val_auc: 0.9692\n", "Epoch 94/100\n", - " - 3s - loss: 0.1944 - accuracy: 0.9209 - auc: 0.9553 - val_loss: 0.3241 - val_accuracy: 0.8659 - val_auc: 0.9554\n", + " - 2s - loss: 0.1514 - accuracy: 0.9376 - auc: 0.9693 - val_loss: 0.2524 - val_accuracy: 0.9041 - val_auc: 0.9694\n", "Epoch 95/100\n", - " - 2s - loss: 0.1900 - accuracy: 0.9228 - auc: 0.9555 - val_loss: 0.3303 - val_accuracy: 0.8668 - val_auc: 0.9556\n", + " - 2s - loss: 0.1488 - accuracy: 0.9381 - auc: 0.9695 - val_loss: 0.2525 - val_accuracy: 0.9025 - val_auc: 0.9695\n", "Epoch 96/100\n", - " - 2s - loss: 0.1893 - accuracy: 0.9227 - auc: 0.9557 - val_loss: 0.3387 - val_accuracy: 0.8664 - val_auc: 0.9558\n", + " - 2s - loss: 0.1494 - accuracy: 0.9386 - auc: 0.9696 - val_loss: 0.2476 - val_accuracy: 0.9025 - val_auc: 0.9697\n", "Epoch 97/100\n", - " - 2s - loss: 0.1892 - accuracy: 0.9234 - auc: 0.9559 - val_loss: 0.3270 - val_accuracy: 0.8664 - val_auc: 0.9560\n", + " - 2s - loss: 0.1525 - accuracy: 0.9388 - auc: 0.9698 - val_loss: 0.2483 - val_accuracy: 0.9015 - val_auc: 0.9699\n", "Epoch 98/100\n", - " - 2s - loss: 0.1903 - accuracy: 0.9241 - auc: 0.9561 - val_loss: 0.3312 - val_accuracy: 0.8659 - val_auc: 0.9562\n", + " - 2s - loss: 0.1505 - accuracy: 0.9382 - auc: 0.9699 - val_loss: 0.2514 - val_accuracy: 0.8994 - val_auc: 0.9700\n", "Epoch 99/100\n", - " - 2s - loss: 0.1871 - accuracy: 0.9225 - auc: 0.9563 - val_loss: 0.3377 - val_accuracy: 0.8659 - val_auc: 0.9564\n", + " - 2s - loss: 0.1479 - accuracy: 0.9399 - auc: 0.9701 - val_loss: 0.2569 - val_accuracy: 0.9036 - val_auc: 0.9702\n", "Epoch 100/100\n", - " - 2s - loss: 0.1904 - accuracy: 0.9226 - auc: 0.9564 - val_loss: 0.3282 - val_accuracy: 0.8650 - val_auc: 0.9565\n" + " - 1s - loss: 0.1469 - accuracy: 0.9400 - auc: 0.9702 - val_loss: 0.2522 - val_accuracy: 0.9030 - val_auc: 0.9703\n" ] } ], "source": [ - "history=model.fit(X_train_resample.drop(['proportion','label'],axis=1).values, y_train_resample.values,\n", + "history=model.fit(X_train_resample.drop(['malwareNum','proportion','label'],axis=1).values, y_train_resample.values,\n", "# validation_split=0.2,\n", - " validation_data=(X_test.drop(['proportion','label'],axis=1).values, y_test),\n", + " validation_data=(X_test.drop(['malwareNum','proportion','label'],axis=1).values, y_test),\n", " verbose=2,\n", " epochs=100,batch_size=32)\n", "# history = model.fit(X_train, y_train, validation_split=0.2,epochs=50,batch_size=32, shuffle=True)" @@ -1290,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1325,12 +1333,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1371,12 +1379,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "## predict using sensor model\n", - "y_pred_with_sensor = model.predict(X_test.drop(['proportion','label'],axis=1).values)\n", + "y_pred_with_sensor = model.predict(X_test.drop(['malwareNum','proportion','label'],axis=1).values)\n", "y_pred_repack_benign = model.predict_proba(repackaged_benign_test_X)\n", "covid_test_X.fillna(0, inplace = True)\n", "# covid_y_pred_w_sensor = model.predict_proba(covid_test_X)" @@ -1391,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1402,7 +1410,7 @@ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_1 (Dense) (None, 48) 15600 \n", + "dense_1 (Dense) (None, 48) 15504 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 48) 0 \n", "_________________________________________________________________\n", @@ -1412,8 +1420,8 @@ "_________________________________________________________________\n", "dense_3 (Dense) (None, 1) 65 \n", "=================================================================\n", - "Total params: 18,801\n", - "Trainable params: 18,801\n", + "Total params: 18,705\n", + "Trainable params: 18,705\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -1423,7 +1431,7 @@ "tf.keras.backend.clear_session()\n", "model_wo = keras.Sequential()\n", "model_wo = Sequential()\n", - "model_wo.add(Dense(48, input_dim=X_train_wo_sensor_resample.shape[1]-2, activation='relu'))\n", + "model_wo.add(Dense(48, input_dim=X_train_wo_sensor_resample.drop(['malwareNum','proportion','label'],axis=1).shape[1], activation='relu'))\n", "model_wo.add(Dropout(0.4))\n", "model_wo.add(Dense(64, activation='relu'))\n", "model_wo.add(Dropout(0.4))\n", @@ -1437,232 +1445,237 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, + "execution_count": 42, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train on 13162 samples, validate on 2230 samples\n", + "Train on 10848 samples, validate on 1908 samples\n", "Epoch 1/100\n", - " - 5s - loss: 0.4577 - accuracy: 0.7815 - auc: 0.7948 - val_loss: 0.3078 - val_accuracy: 0.8525 - val_auc: 0.8724\n", + " - 3s - loss: 0.4458 - accuracy: 0.7771 - auc: 0.7864 - val_loss: 0.3259 - val_accuracy: 0.8664 - val_auc: 0.8783\n", "Epoch 2/100\n", - " - 2s - loss: 0.3354 - accuracy: 0.8525 - auc: 0.8932 - val_loss: 0.3167 - val_accuracy: 0.8386 - val_auc: 0.9055\n", + " - 2s - loss: 0.3112 - accuracy: 0.8625 - auc: 0.8999 - val_loss: 0.3154 - val_accuracy: 0.8611 - val_auc: 0.9148\n", "Epoch 3/100\n", - " - 2s - loss: 0.2936 - accuracy: 0.8721 - auc: 0.9133 - val_loss: 0.2802 - val_accuracy: 0.8704 - val_auc: 0.9203\n", + " - 1s - loss: 0.2791 - accuracy: 0.8811 - auc: 0.9224 - val_loss: 0.2824 - val_accuracy: 0.8742 - val_auc: 0.9289\n", "Epoch 4/100\n", - " - 3s - loss: 0.2766 - accuracy: 0.8821 - auc: 0.9247 - val_loss: 0.3268 - val_accuracy: 0.8547 - val_auc: 0.9286\n", + " - 2s - loss: 0.2574 - accuracy: 0.8918 - auc: 0.9335 - val_loss: 0.2737 - val_accuracy: 0.8768 - val_auc: 0.9375\n", "Epoch 5/100\n", - " - 3s - loss: 0.2647 - accuracy: 0.8873 - auc: 0.9316 - val_loss: 0.3026 - val_accuracy: 0.8561 - val_auc: 0.9339\n", + " - 2s - loss: 0.2405 - accuracy: 0.8977 - auc: 0.9403 - val_loss: 0.2766 - val_accuracy: 0.8700 - val_auc: 0.9432\n", "Epoch 6/100\n", - " - 2s - loss: 0.2564 - accuracy: 0.8933 - auc: 0.9360 - val_loss: 0.2985 - val_accuracy: 0.8605 - val_auc: 0.9379\n", + " - 2s - loss: 0.2343 - accuracy: 0.9023 - auc: 0.9451 - val_loss: 0.2708 - val_accuracy: 0.8810 - val_auc: 0.9472\n", "Epoch 7/100\n", - " - 3s - loss: 0.2492 - accuracy: 0.8923 - auc: 0.9395 - val_loss: 0.2912 - val_accuracy: 0.8637 - val_auc: 0.9410\n", + " - 2s - loss: 0.2246 - accuracy: 0.9075 - auc: 0.9488 - val_loss: 0.2712 - val_accuracy: 0.8747 - val_auc: 0.9503\n", "Epoch 8/100\n", - " - 3s - loss: 0.2434 - accuracy: 0.8959 - auc: 0.9423 - val_loss: 0.3246 - val_accuracy: 0.8529 - val_auc: 0.9434\n", + " - 2s - loss: 0.2157 - accuracy: 0.9129 - auc: 0.9517 - val_loss: 0.2808 - val_accuracy: 0.8737 - val_auc: 0.9529\n", "Epoch 9/100\n", - " - 3s - loss: 0.2372 - accuracy: 0.9001 - auc: 0.9444 - val_loss: 0.3010 - val_accuracy: 0.8677 - val_auc: 0.9454\n", + " - 2s - loss: 0.2080 - accuracy: 0.9129 - auc: 0.9541 - val_loss: 0.2637 - val_accuracy: 0.8816 - val_auc: 0.9551\n", "Epoch 10/100\n", - " - 2s - loss: 0.2345 - accuracy: 0.9019 - auc: 0.9464 - val_loss: 0.2914 - val_accuracy: 0.8664 - val_auc: 0.9473\n", + " - 2s - loss: 0.2090 - accuracy: 0.9126 - auc: 0.9560 - val_loss: 0.2877 - val_accuracy: 0.8758 - val_auc: 0.9568\n", "Epoch 11/100\n", - " - 2s - loss: 0.2319 - accuracy: 0.9001 - auc: 0.9481 - val_loss: 0.3529 - val_accuracy: 0.8439 - val_auc: 0.9487\n", + " - 2s - loss: 0.2030 - accuracy: 0.9120 - auc: 0.9575 - val_loss: 0.2860 - val_accuracy: 0.8800 - val_auc: 0.9582\n", "Epoch 12/100\n", - " - 2s - loss: 0.2263 - accuracy: 0.9046 - auc: 0.9493 - val_loss: 0.3306 - val_accuracy: 0.8574 - val_auc: 0.9499\n", + " - 2s - loss: 0.1925 - accuracy: 0.9194 - auc: 0.9589 - val_loss: 0.2956 - val_accuracy: 0.8810 - val_auc: 0.9596\n", "Epoch 13/100\n", - " - 2s - loss: 0.2247 - accuracy: 0.9050 - auc: 0.9505 - val_loss: 0.2995 - val_accuracy: 0.8704 - val_auc: 0.9511\n", + " - 2s - loss: 0.1930 - accuracy: 0.9185 - auc: 0.9603 - val_loss: 0.2923 - val_accuracy: 0.8816 - val_auc: 0.9608\n", "Epoch 14/100\n", - " - 2s - loss: 0.2225 - accuracy: 0.9050 - auc: 0.9516 - val_loss: 0.3209 - val_accuracy: 0.8619 - val_auc: 0.9521\n", + " - 2s - loss: 0.1895 - accuracy: 0.9202 - auc: 0.9613 - val_loss: 0.2782 - val_accuracy: 0.8894 - val_auc: 0.9618\n", "Epoch 15/100\n", - " - 2s - loss: 0.2213 - accuracy: 0.9046 - auc: 0.9526 - val_loss: 0.3239 - val_accuracy: 0.8605 - val_auc: 0.9530\n", + " - 2s - loss: 0.1872 - accuracy: 0.9229 - auc: 0.9624 - val_loss: 0.2903 - val_accuracy: 0.8857 - val_auc: 0.9628\n", "Epoch 16/100\n", - " - 2s - loss: 0.2172 - accuracy: 0.9068 - auc: 0.9534 - val_loss: 0.3211 - val_accuracy: 0.8619 - val_auc: 0.9538\n", + " - 2s - loss: 0.1867 - accuracy: 0.9203 - auc: 0.9632 - val_loss: 0.2877 - val_accuracy: 0.8842 - val_auc: 0.9636\n", "Epoch 17/100\n", - " - 2s - loss: 0.2190 - accuracy: 0.9068 - auc: 0.9542 - val_loss: 0.3144 - val_accuracy: 0.8664 - val_auc: 0.9546\n", + " - 2s - loss: 0.1835 - accuracy: 0.9221 - auc: 0.9640 - val_loss: 0.2858 - val_accuracy: 0.8905 - val_auc: 0.9644\n", "Epoch 18/100\n", - " - 2s - loss: 0.2095 - accuracy: 0.9091 - auc: 0.9550 - val_loss: 0.3202 - val_accuracy: 0.8682 - val_auc: 0.9554\n", + " - 2s - loss: 0.1816 - accuracy: 0.9239 - auc: 0.9648 - val_loss: 0.3151 - val_accuracy: 0.8747 - val_auc: 0.9651\n", "Epoch 19/100\n", - " - 2s - loss: 0.2135 - accuracy: 0.9076 - auc: 0.9557 - val_loss: 0.3294 - val_accuracy: 0.8605 - val_auc: 0.9560\n", + " - 2s - loss: 0.1790 - accuracy: 0.9233 - auc: 0.9654 - val_loss: 0.2872 - val_accuracy: 0.8947 - val_auc: 0.9657\n", "Epoch 20/100\n", - " - 2s - loss: 0.2107 - accuracy: 0.9081 - auc: 0.9564 - val_loss: 0.3277 - val_accuracy: 0.8726 - val_auc: 0.9566\n", + " - 2s - loss: 0.1789 - accuracy: 0.9251 - auc: 0.9660 - val_loss: 0.3034 - val_accuracy: 0.8842 - val_auc: 0.9663\n", "Epoch 21/100\n", - " - 2s - loss: 0.2092 - accuracy: 0.9108 - auc: 0.9569 - val_loss: 0.3147 - val_accuracy: 0.8753 - val_auc: 0.9572\n", + " - 2s - loss: 0.1721 - accuracy: 0.9269 - auc: 0.9666 - val_loss: 0.3260 - val_accuracy: 0.8774 - val_auc: 0.9668\n", "Epoch 22/100\n", - " - 2s - loss: 0.2060 - accuracy: 0.9096 - auc: 0.9575 - val_loss: 0.3303 - val_accuracy: 0.8628 - val_auc: 0.9578\n", + " - 2s - loss: 0.1728 - accuracy: 0.9248 - auc: 0.9671 - val_loss: 0.3010 - val_accuracy: 0.8873 - val_auc: 0.9673\n", "Epoch 23/100\n", - " - 2s - loss: 0.2081 - accuracy: 0.9102 - auc: 0.9581 - val_loss: 0.3245 - val_accuracy: 0.8735 - val_auc: 0.9583\n", + " - 2s - loss: 0.1736 - accuracy: 0.9254 - auc: 0.9676 - val_loss: 0.3114 - val_accuracy: 0.8847 - val_auc: 0.9678\n", "Epoch 24/100\n", - " - 2s - loss: 0.2005 - accuracy: 0.9094 - auc: 0.9586 - val_loss: 0.3393 - val_accuracy: 0.8632 - val_auc: 0.9588\n", + " - 2s - loss: 0.1721 - accuracy: 0.9283 - auc: 0.9680 - val_loss: 0.3084 - val_accuracy: 0.8857 - val_auc: 0.9683\n", "Epoch 25/100\n", - " - 2s - loss: 0.2032 - accuracy: 0.9121 - auc: 0.9590 - val_loss: 0.3468 - val_accuracy: 0.8646 - val_auc: 0.9592\n", + " - 2s - loss: 0.1658 - accuracy: 0.9294 - auc: 0.9685 - val_loss: 0.3103 - val_accuracy: 0.8884 - val_auc: 0.9687\n", "Epoch 26/100\n", - " - 2s - loss: 0.2036 - accuracy: 0.9095 - auc: 0.9594 - val_loss: 0.3541 - val_accuracy: 0.8614 - val_auc: 0.9596\n", + " - 2s - loss: 0.1672 - accuracy: 0.9274 - auc: 0.9689 - val_loss: 0.3233 - val_accuracy: 0.8905 - val_auc: 0.9691\n", "Epoch 27/100\n", - " - 2s - loss: 0.2012 - accuracy: 0.9126 - auc: 0.9598 - val_loss: 0.3404 - val_accuracy: 0.8659 - val_auc: 0.9600\n", + " - 2s - loss: 0.1633 - accuracy: 0.9294 - auc: 0.9693 - val_loss: 0.3225 - val_accuracy: 0.8878 - val_auc: 0.9695\n", "Epoch 28/100\n", - " - 2s - loss: 0.1966 - accuracy: 0.9148 - auc: 0.9602 - val_loss: 0.3470 - val_accuracy: 0.8664 - val_auc: 0.9604\n", + " - 2s - loss: 0.1636 - accuracy: 0.9290 - auc: 0.9697 - val_loss: 0.3360 - val_accuracy: 0.8899 - val_auc: 0.9699\n", "Epoch 29/100\n", - " - 2s - loss: 0.2023 - accuracy: 0.9116 - auc: 0.9606 - val_loss: 0.3410 - val_accuracy: 0.8655 - val_auc: 0.9607\n", + " - 2s - loss: 0.1683 - accuracy: 0.9263 - auc: 0.9700 - val_loss: 0.3296 - val_accuracy: 0.8926 - val_auc: 0.9702\n", "Epoch 30/100\n", - " - 2s - loss: 0.1980 - accuracy: 0.9138 - auc: 0.9609 - val_loss: 0.3335 - val_accuracy: 0.8686 - val_auc: 0.9611\n", + " - 2s - loss: 0.1648 - accuracy: 0.9285 - auc: 0.9703 - val_loss: 0.3079 - val_accuracy: 0.8878 - val_auc: 0.9705\n", "Epoch 31/100\n", - " - 2s - loss: 0.1962 - accuracy: 0.9152 - auc: 0.9613 - val_loss: 0.3439 - val_accuracy: 0.8655 - val_auc: 0.9614\n", + " - 2s - loss: 0.1596 - accuracy: 0.9306 - auc: 0.9706 - val_loss: 0.3391 - val_accuracy: 0.8884 - val_auc: 0.9708\n", "Epoch 32/100\n", - " - 2s - loss: 0.1978 - accuracy: 0.9145 - auc: 0.9616 - val_loss: 0.3591 - val_accuracy: 0.8574 - val_auc: 0.9617\n", + " - 2s - loss: 0.1665 - accuracy: 0.9267 - auc: 0.9709 - val_loss: 0.3428 - val_accuracy: 0.8878 - val_auc: 0.9710\n", "Epoch 33/100\n", - " - 2s - loss: 0.1968 - accuracy: 0.9128 - auc: 0.9618 - val_loss: 0.3519 - val_accuracy: 0.8646 - val_auc: 0.9620\n", + " - 2s - loss: 0.1615 - accuracy: 0.9298 - auc: 0.9712 - val_loss: 0.3385 - val_accuracy: 0.8847 - val_auc: 0.9713\n", "Epoch 34/100\n", - " - 2s - loss: 0.1951 - accuracy: 0.9147 - auc: 0.9621 - val_loss: 0.3540 - val_accuracy: 0.8655 - val_auc: 0.9623\n", + " - 2s - loss: 0.1605 - accuracy: 0.9296 - auc: 0.9714 - val_loss: 0.3416 - val_accuracy: 0.8884 - val_auc: 0.9715\n", "Epoch 35/100\n", - " - 2s - loss: 0.1972 - accuracy: 0.9122 - auc: 0.9624 - val_loss: 0.3524 - val_accuracy: 0.8691 - val_auc: 0.9625\n", + " - 2s - loss: 0.1514 - accuracy: 0.9335 - auc: 0.9717 - val_loss: 0.3589 - val_accuracy: 0.8863 - val_auc: 0.9718\n", "Epoch 36/100\n", - " - 2s - loss: 0.1928 - accuracy: 0.9129 - auc: 0.9627 - val_loss: 0.3664 - val_accuracy: 0.8682 - val_auc: 0.9628\n", + " - 2s - loss: 0.1597 - accuracy: 0.9292 - auc: 0.9719 - val_loss: 0.3412 - val_accuracy: 0.8889 - val_auc: 0.9721\n", "Epoch 37/100\n", - " - 2s - loss: 0.1956 - accuracy: 0.9118 - auc: 0.9629 - val_loss: 0.3737 - val_accuracy: 0.8744 - val_auc: 0.9630\n", + " - 2s - loss: 0.1597 - accuracy: 0.9294 - auc: 0.9722 - val_loss: 0.3632 - val_accuracy: 0.8889 - val_auc: 0.9723\n", "Epoch 38/100\n", - " - 2s - loss: 0.1907 - accuracy: 0.9132 - auc: 0.9631 - val_loss: 0.3668 - val_accuracy: 0.8668 - val_auc: 0.9632\n", + " - 2s - loss: 0.1562 - accuracy: 0.9278 - auc: 0.9724 - val_loss: 0.3584 - val_accuracy: 0.8962 - val_auc: 0.9725\n", "Epoch 39/100\n", - " - 2s - loss: 0.1936 - accuracy: 0.9135 - auc: 0.9633 - val_loss: 0.3628 - val_accuracy: 0.8758 - val_auc: 0.9634\n", + " - 2s - loss: 0.1564 - accuracy: 0.9298 - auc: 0.9726 - val_loss: 0.3560 - val_accuracy: 0.8899 - val_auc: 0.9727\n", "Epoch 40/100\n", - " - 2s - loss: 0.1920 - accuracy: 0.9124 - auc: 0.9636 - val_loss: 0.3586 - val_accuracy: 0.8659 - val_auc: 0.9637\n", + " - 2s - loss: 0.1572 - accuracy: 0.9288 - auc: 0.9728 - val_loss: 0.3695 - val_accuracy: 0.8842 - val_auc: 0.9729\n", "Epoch 41/100\n", - " - 2s - loss: 0.1880 - accuracy: 0.9160 - auc: 0.9638 - val_loss: 0.3769 - val_accuracy: 0.8744 - val_auc: 0.9639\n", + " - 2s - loss: 0.1576 - accuracy: 0.9288 - auc: 0.9730 - val_loss: 0.3756 - val_accuracy: 0.8878 - val_auc: 0.9730\n", "Epoch 42/100\n", - " - 2s - loss: 0.1887 - accuracy: 0.9154 - auc: 0.9640 - val_loss: 0.3735 - val_accuracy: 0.8700 - val_auc: 0.9641\n", + " - 2s - loss: 0.1563 - accuracy: 0.9298 - auc: 0.9731 - val_loss: 0.3643 - val_accuracy: 0.8852 - val_auc: 0.9732\n", "Epoch 43/100\n", - " - 2s - loss: 0.1896 - accuracy: 0.9155 - auc: 0.9642 - val_loss: 0.3764 - val_accuracy: 0.8713 - val_auc: 0.9643\n", + " - 2s - loss: 0.1493 - accuracy: 0.9327 - auc: 0.9733 - val_loss: 0.3826 - val_accuracy: 0.8821 - val_auc: 0.9734\n", "Epoch 44/100\n", - " - 2s - loss: 0.1857 - accuracy: 0.9184 - auc: 0.9644 - val_loss: 0.3790 - val_accuracy: 0.8731 - val_auc: 0.9645\n", + " - 2s - loss: 0.1480 - accuracy: 0.9320 - auc: 0.9735 - val_loss: 0.3962 - val_accuracy: 0.8899 - val_auc: 0.9736\n", "Epoch 45/100\n", - " - 2s - loss: 0.1867 - accuracy: 0.9148 - auc: 0.9646 - val_loss: 0.4001 - val_accuracy: 0.8655 - val_auc: 0.9647\n", + " - 2s - loss: 0.1523 - accuracy: 0.9308 - auc: 0.9737 - val_loss: 0.3826 - val_accuracy: 0.8931 - val_auc: 0.9737\n", "Epoch 46/100\n", - " - 2s - loss: 0.1871 - accuracy: 0.9164 - auc: 0.9648 - val_loss: 0.3898 - val_accuracy: 0.8686 - val_auc: 0.9648\n", + " - 2s - loss: 0.1550 - accuracy: 0.9319 - auc: 0.9738 - val_loss: 0.4065 - val_accuracy: 0.8784 - val_auc: 0.9739\n", "Epoch 47/100\n", - " - 2s - loss: 0.1866 - accuracy: 0.9180 - auc: 0.9649 - val_loss: 0.3781 - val_accuracy: 0.8668 - val_auc: 0.9650\n", + " - 2s - loss: 0.1499 - accuracy: 0.9332 - auc: 0.9739 - val_loss: 0.4217 - val_accuracy: 0.8899 - val_auc: 0.9740\n", "Epoch 48/100\n", - " - 2s - loss: 0.1869 - accuracy: 0.9151 - auc: 0.9651 - val_loss: 0.4157 - val_accuracy: 0.8668 - val_auc: 0.9652\n", + " - 2s - loss: 0.1491 - accuracy: 0.9320 - auc: 0.9741 - val_loss: 0.3908 - val_accuracy: 0.8926 - val_auc: 0.9742\n", "Epoch 49/100\n", - " - 2s - loss: 0.1844 - accuracy: 0.9201 - auc: 0.9652 - val_loss: 0.3963 - val_accuracy: 0.8619 - val_auc: 0.9653\n", + " - 2s - loss: 0.1461 - accuracy: 0.9318 - auc: 0.9743 - val_loss: 0.4204 - val_accuracy: 0.8905 - val_auc: 0.9743\n", "Epoch 50/100\n", - " - 2s - loss: 0.1813 - accuracy: 0.9178 - auc: 0.9654 - val_loss: 0.4167 - val_accuracy: 0.8655 - val_auc: 0.9655\n", + " - 2s - loss: 0.1514 - accuracy: 0.9294 - auc: 0.9744 - val_loss: 0.4178 - val_accuracy: 0.8889 - val_auc: 0.9744\n", "Epoch 51/100\n", - " - 2s - loss: 0.1831 - accuracy: 0.9205 - auc: 0.9656 - val_loss: 0.3999 - val_accuracy: 0.8686 - val_auc: 0.9656\n", + " - 2s - loss: 0.1519 - accuracy: 0.9343 - auc: 0.9745 - val_loss: 0.3970 - val_accuracy: 0.8915 - val_auc: 0.9746\n", "Epoch 52/100\n", - " - 2s - loss: 0.1811 - accuracy: 0.9180 - auc: 0.9657 - val_loss: 0.4207 - val_accuracy: 0.8704 - val_auc: 0.9658\n", + " - 2s - loss: 0.1448 - accuracy: 0.9332 - auc: 0.9746 - val_loss: 0.4135 - val_accuracy: 0.8894 - val_auc: 0.9747\n", "Epoch 53/100\n", - " - 2s - loss: 0.1832 - accuracy: 0.9179 - auc: 0.9659 - val_loss: 0.4232 - val_accuracy: 0.8565 - val_auc: 0.9659\n", + " - 2s - loss: 0.1485 - accuracy: 0.9329 - auc: 0.9748 - val_loss: 0.4029 - val_accuracy: 0.8873 - val_auc: 0.9748\n", "Epoch 54/100\n", - " - 2s - loss: 0.1859 - accuracy: 0.9176 - auc: 0.9660 - val_loss: 0.4039 - val_accuracy: 0.8655 - val_auc: 0.9660\n", + " - 2s - loss: 0.1450 - accuracy: 0.9346 - auc: 0.9749 - val_loss: 0.4273 - val_accuracy: 0.8905 - val_auc: 0.9750\n", "Epoch 55/100\n", - " - 2s - loss: 0.1834 - accuracy: 0.9214 - auc: 0.9661 - val_loss: 0.4342 - val_accuracy: 0.8623 - val_auc: 0.9662\n", + " - 2s - loss: 0.1466 - accuracy: 0.9313 - auc: 0.9750 - val_loss: 0.4343 - val_accuracy: 0.8920 - val_auc: 0.9751\n", "Epoch 56/100\n", - " - 2s - loss: 0.1810 - accuracy: 0.9183 - auc: 0.9663 - val_loss: 0.4308 - val_accuracy: 0.8596 - val_auc: 0.9663\n", + " - 2s - loss: 0.1457 - accuracy: 0.9322 - auc: 0.9751 - val_loss: 0.4120 - val_accuracy: 0.8905 - val_auc: 0.9752\n", "Epoch 57/100\n", - " - 2s - loss: 0.1800 - accuracy: 0.9198 - auc: 0.9664 - val_loss: 0.4271 - val_accuracy: 0.8722 - val_auc: 0.9664\n", + " - 2s - loss: 0.1474 - accuracy: 0.9320 - auc: 0.9752 - val_loss: 0.4314 - val_accuracy: 0.8905 - val_auc: 0.9753\n", "Epoch 58/100\n", - " - 2s - loss: 0.1818 - accuracy: 0.9186 - auc: 0.9665 - val_loss: 0.4489 - val_accuracy: 0.8700 - val_auc: 0.9666\n", + " - 2s - loss: 0.1490 - accuracy: 0.9306 - auc: 0.9753 - val_loss: 0.4135 - val_accuracy: 0.8931 - val_auc: 0.9754\n", "Epoch 59/100\n", - " - 2s - loss: 0.1826 - accuracy: 0.9160 - auc: 0.9666 - val_loss: 0.4270 - val_accuracy: 0.8722 - val_auc: 0.9667\n", + " - 2s - loss: 0.1460 - accuracy: 0.9306 - auc: 0.9754 - val_loss: 0.4231 - val_accuracy: 0.8905 - val_auc: 0.9755\n", "Epoch 60/100\n", - " - 2s - loss: 0.1796 - accuracy: 0.9181 - auc: 0.9667 - val_loss: 0.4211 - val_accuracy: 0.8673 - val_auc: 0.9668\n", + " - 2s - loss: 0.1464 - accuracy: 0.9345 - auc: 0.9755 - val_loss: 0.4168 - val_accuracy: 0.8910 - val_auc: 0.9756\n", "Epoch 61/100\n", - " - 2s - loss: 0.1801 - accuracy: 0.9186 - auc: 0.9669 - val_loss: 0.4325 - val_accuracy: 0.8637 - val_auc: 0.9669\n", + " - 2s - loss: 0.1464 - accuracy: 0.9346 - auc: 0.9756 - val_loss: 0.4375 - val_accuracy: 0.8905 - val_auc: 0.9757\n", "Epoch 62/100\n", - " - 2s - loss: 0.1814 - accuracy: 0.9192 - auc: 0.9669 - val_loss: 0.4259 - val_accuracy: 0.8700 - val_auc: 0.9670\n", + " - 2s - loss: 0.1457 - accuracy: 0.9320 - auc: 0.9758 - val_loss: 0.4276 - val_accuracy: 0.8905 - val_auc: 0.9758\n", "Epoch 63/100\n", - " - 2s - loss: 0.1795 - accuracy: 0.9192 - auc: 0.9671 - val_loss: 0.4131 - val_accuracy: 0.8709 - val_auc: 0.9671\n", + " - 2s - loss: 0.1432 - accuracy: 0.9348 - auc: 0.9758 - val_loss: 0.4433 - val_accuracy: 0.8915 - val_auc: 0.9759\n", "Epoch 64/100\n", - " - 2s - loss: 0.1803 - accuracy: 0.9200 - auc: 0.9672 - val_loss: 0.4356 - val_accuracy: 0.8713 - val_auc: 0.9672\n", + " - 2s - loss: 0.1427 - accuracy: 0.9347 - auc: 0.9759 - val_loss: 0.4374 - val_accuracy: 0.8952 - val_auc: 0.9760\n", "Epoch 65/100\n", - " - 2s - loss: 0.1770 - accuracy: 0.9167 - auc: 0.9673 - val_loss: 0.4479 - val_accuracy: 0.8700 - val_auc: 0.9673\n", + " - 2s - loss: 0.1440 - accuracy: 0.9346 - auc: 0.9760 - val_loss: 0.4399 - val_accuracy: 0.8978 - val_auc: 0.9761\n", "Epoch 66/100\n", - " - 2s - loss: 0.1747 - accuracy: 0.9198 - auc: 0.9674 - val_loss: 0.4576 - val_accuracy: 0.8695 - val_auc: 0.9674\n", + " - 2s - loss: 0.1448 - accuracy: 0.9338 - auc: 0.9761 - val_loss: 0.4254 - val_accuracy: 0.8816 - val_auc: 0.9761\n", "Epoch 67/100\n", - " - 2s - loss: 0.1762 - accuracy: 0.9189 - auc: 0.9675 - val_loss: 0.4516 - val_accuracy: 0.8709 - val_auc: 0.9675\n", + " - 2s - loss: 0.1423 - accuracy: 0.9342 - auc: 0.9762 - val_loss: 0.4541 - val_accuracy: 0.8952 - val_auc: 0.9762\n", "Epoch 68/100\n", - " - 3s - loss: 0.1788 - accuracy: 0.9199 - auc: 0.9676 - val_loss: 0.4403 - val_accuracy: 0.8650 - val_auc: 0.9676\n", + " - 2s - loss: 0.1435 - accuracy: 0.9346 - auc: 0.9763 - val_loss: 0.4525 - val_accuracy: 0.8910 - val_auc: 0.9763\n", "Epoch 69/100\n", - " - 3s - loss: 0.1760 - accuracy: 0.9207 - auc: 0.9677 - val_loss: 0.4781 - val_accuracy: 0.8655 - val_auc: 0.9677\n", + " - 2s - loss: 0.1465 - accuracy: 0.9320 - auc: 0.9763 - val_loss: 0.4446 - val_accuracy: 0.8920 - val_auc: 0.9764\n", "Epoch 70/100\n", - " - 2s - loss: 0.1773 - accuracy: 0.9207 - auc: 0.9677 - val_loss: 0.4484 - val_accuracy: 0.8655 - val_auc: 0.9678\n", + " - 2s - loss: 0.1383 - accuracy: 0.9350 - auc: 0.9764 - val_loss: 0.4729 - val_accuracy: 0.8920 - val_auc: 0.9765\n", "Epoch 71/100\n", - " - 2s - loss: 0.1779 - accuracy: 0.9203 - auc: 0.9678 - val_loss: 0.4128 - val_accuracy: 0.8713 - val_auc: 0.9679\n", + " - 2s - loss: 0.1377 - accuracy: 0.9347 - auc: 0.9765 - val_loss: 0.4733 - val_accuracy: 0.8957 - val_auc: 0.9766\n", "Epoch 72/100\n", - " - 2s - loss: 0.1741 - accuracy: 0.9214 - auc: 0.9679 - val_loss: 0.4663 - val_accuracy: 0.8655 - val_auc: 0.9680\n", + " - 2s - loss: 0.1416 - accuracy: 0.9334 - auc: 0.9766 - val_loss: 0.4516 - val_accuracy: 0.8931 - val_auc: 0.9766\n", "Epoch 73/100\n", - " - 2s - loss: 0.1766 - accuracy: 0.9214 - auc: 0.9680 - val_loss: 0.4110 - val_accuracy: 0.8717 - val_auc: 0.9680\n", + " - 2s - loss: 0.1385 - accuracy: 0.9343 - auc: 0.9767 - val_loss: 0.5185 - val_accuracy: 0.8899 - val_auc: 0.9767\n", "Epoch 74/100\n", - " - 2s - loss: 0.1724 - accuracy: 0.9215 - auc: 0.9681 - val_loss: 0.4861 - val_accuracy: 0.8722 - val_auc: 0.9681\n", + " - 2s - loss: 0.1468 - accuracy: 0.9345 - auc: 0.9767 - val_loss: 0.4422 - val_accuracy: 0.8910 - val_auc: 0.9768\n", "Epoch 75/100\n", - " - 2s - loss: 0.1777 - accuracy: 0.9199 - auc: 0.9682 - val_loss: 0.4728 - val_accuracy: 0.8762 - val_auc: 0.9682\n", + " - 2s - loss: 0.1402 - accuracy: 0.9363 - auc: 0.9768 - val_loss: 0.4701 - val_accuracy: 0.8884 - val_auc: 0.9768\n", "Epoch 76/100\n", - " - 2s - loss: 0.1700 - accuracy: 0.9233 - auc: 0.9683 - val_loss: 0.4779 - val_accuracy: 0.8749 - val_auc: 0.9683\n", + " - 2s - loss: 0.1395 - accuracy: 0.9357 - auc: 0.9769 - val_loss: 0.4797 - val_accuracy: 0.8905 - val_auc: 0.9769\n", "Epoch 77/100\n", - " - 2s - loss: 0.1751 - accuracy: 0.9188 - auc: 0.9684 - val_loss: 0.4693 - val_accuracy: 0.8668 - val_auc: 0.9684\n", + " - 2s - loss: 0.1444 - accuracy: 0.9311 - auc: 0.9769 - val_loss: 0.4863 - val_accuracy: 0.8941 - val_auc: 0.9769\n", "Epoch 78/100\n", - " - 2s - loss: 0.1745 - accuracy: 0.9187 - auc: 0.9684 - val_loss: 0.4549 - val_accuracy: 0.8758 - val_auc: 0.9685\n", + " - 2s - loss: 0.1412 - accuracy: 0.9348 - auc: 0.9770 - val_loss: 0.4540 - val_accuracy: 0.8894 - val_auc: 0.9770\n", "Epoch 79/100\n", - " - 2s - loss: 0.1749 - accuracy: 0.9191 - auc: 0.9685 - val_loss: 0.4467 - val_accuracy: 0.8744 - val_auc: 0.9686\n", + " - 2s - loss: 0.1376 - accuracy: 0.9347 - auc: 0.9770 - val_loss: 0.4912 - val_accuracy: 0.8868 - val_auc: 0.9771\n", "Epoch 80/100\n", - " - 2s - loss: 0.1716 - accuracy: 0.9209 - auc: 0.9686 - val_loss: 0.5058 - val_accuracy: 0.8704 - val_auc: 0.9686\n", + " - 2s - loss: 0.1438 - accuracy: 0.9308 - auc: 0.9771 - val_loss: 0.4600 - val_accuracy: 0.8878 - val_auc: 0.9771\n", "Epoch 81/100\n", - " - 2s - loss: 0.1755 - accuracy: 0.9200 - auc: 0.9687 - val_loss: 0.4596 - val_accuracy: 0.8717 - val_auc: 0.9687\n", + " - 2s - loss: 0.1385 - accuracy: 0.9349 - auc: 0.9771 - val_loss: 0.4933 - val_accuracy: 0.8889 - val_auc: 0.9772\n", "Epoch 82/100\n", - " - 2s - loss: 0.1783 - accuracy: 0.9195 - auc: 0.9687 - val_loss: 0.4513 - val_accuracy: 0.8709 - val_auc: 0.9688\n", + " - 2s - loss: 0.1377 - accuracy: 0.9355 - auc: 0.9772 - val_loss: 0.4626 - val_accuracy: 0.8920 - val_auc: 0.9772\n", "Epoch 83/100\n", - " - 2s - loss: 0.1743 - accuracy: 0.9172 - auc: 0.9688 - val_loss: 0.4850 - val_accuracy: 0.8771 - val_auc: 0.9688\n", + " - 2s - loss: 0.1387 - accuracy: 0.9339 - auc: 0.9773 - val_loss: 0.4790 - val_accuracy: 0.8910 - val_auc: 0.9773\n", "Epoch 84/100\n", - " - 2s - loss: 0.1705 - accuracy: 0.9220 - auc: 0.9689 - val_loss: 0.4736 - val_accuracy: 0.8717 - val_auc: 0.9689\n", + " - 2s - loss: 0.1367 - accuracy: 0.9361 - auc: 0.9773 - val_loss: 0.5121 - val_accuracy: 0.8847 - val_auc: 0.9773\n", "Epoch 85/100\n", - " - 2s - loss: 0.1737 - accuracy: 0.9173 - auc: 0.9690 - val_loss: 0.4596 - val_accuracy: 0.8731 - val_auc: 0.9690\n", + " - 2s - loss: 0.1346 - accuracy: 0.9344 - auc: 0.9774 - val_loss: 0.5133 - val_accuracy: 0.8931 - val_auc: 0.9774\n", "Epoch 86/100\n", - " - 2s - loss: 0.1696 - accuracy: 0.9215 - auc: 0.9690 - val_loss: 0.4649 - val_accuracy: 0.8682 - val_auc: 0.9691\n", + " - 2s - loss: 0.1391 - accuracy: 0.9332 - auc: 0.9774 - val_loss: 0.4980 - val_accuracy: 0.8915 - val_auc: 0.9775\n", "Epoch 87/100\n", - " - 2s - loss: 0.1723 - accuracy: 0.9215 - auc: 0.9691 - val_loss: 0.4383 - val_accuracy: 0.8673 - val_auc: 0.9691\n", + " - 2s - loss: 0.1400 - accuracy: 0.9338 - auc: 0.9775 - val_loss: 0.4740 - val_accuracy: 0.8941 - val_auc: 0.9775\n", "Epoch 88/100\n", - " - 2s - loss: 0.1737 - accuracy: 0.9195 - auc: 0.9692 - val_loss: 0.4937 - val_accuracy: 0.8596 - val_auc: 0.9692\n", + " - 2s - loss: 0.1399 - accuracy: 0.9354 - auc: 0.9775 - val_loss: 0.5174 - val_accuracy: 0.8915 - val_auc: 0.9776\n", "Epoch 89/100\n", - " - 2s - loss: 0.1733 - accuracy: 0.9210 - auc: 0.9692 - val_loss: 0.4626 - val_accuracy: 0.8758 - val_auc: 0.9693\n", + " - 2s - loss: 0.1393 - accuracy: 0.9334 - auc: 0.9776 - val_loss: 0.5047 - val_accuracy: 0.8857 - val_auc: 0.9776\n", "Epoch 90/100\n", - " - 2s - loss: 0.1696 - accuracy: 0.9194 - auc: 0.9693 - val_loss: 0.4623 - val_accuracy: 0.8677 - val_auc: 0.9693\n", + " - 2s - loss: 0.1332 - accuracy: 0.9373 - auc: 0.9776 - val_loss: 0.5035 - val_accuracy: 0.8889 - val_auc: 0.9776\n", "Epoch 91/100\n", - " - 2s - loss: 0.1683 - accuracy: 0.9199 - auc: 0.9694 - val_loss: 0.4461 - val_accuracy: 0.8735 - val_auc: 0.9694\n", + " - 2s - loss: 0.1377 - accuracy: 0.9334 - auc: 0.9777 - val_loss: 0.5216 - val_accuracy: 0.8873 - val_auc: 0.9777\n", "Epoch 92/100\n", - " - 2s - loss: 0.1710 - accuracy: 0.9171 - auc: 0.9694 - val_loss: 0.4808 - val_accuracy: 0.8731 - val_auc: 0.9695\n", + " - 2s - loss: 0.1401 - accuracy: 0.9346 - auc: 0.9777 - val_loss: 0.5004 - val_accuracy: 0.8926 - val_auc: 0.9777\n", "Epoch 93/100\n", - " - 2s - loss: 0.1677 - accuracy: 0.9181 - auc: 0.9695 - val_loss: 0.5076 - val_accuracy: 0.8664 - val_auc: 0.9695\n", + " - 2s - loss: 0.1376 - accuracy: 0.9364 - auc: 0.9778 - val_loss: 0.5153 - val_accuracy: 0.8915 - val_auc: 0.9778\n", "Epoch 94/100\n", - " - 2s - loss: 0.1714 - accuracy: 0.9207 - auc: 0.9696 - val_loss: 0.5061 - val_accuracy: 0.8744 - val_auc: 0.9696\n", + " - 2s - loss: 0.1355 - accuracy: 0.9381 - auc: 0.9778 - val_loss: 0.5621 - val_accuracy: 0.8894 - val_auc: 0.9778\n", "Epoch 95/100\n", - " - 2s - loss: 0.1651 - accuracy: 0.9228 - auc: 0.9696 - val_loss: 0.5127 - val_accuracy: 0.8700 - val_auc: 0.9697\n", + " - 2s - loss: 0.1353 - accuracy: 0.9390 - auc: 0.9778 - val_loss: 0.5273 - val_accuracy: 0.8878 - val_auc: 0.9779\n", "Epoch 96/100\n", - " - 2s - loss: 0.1709 - accuracy: 0.9202 - auc: 0.9697 - val_loss: 0.5105 - val_accuracy: 0.8700 - val_auc: 0.9697\n", + " - 2s - loss: 0.1340 - accuracy: 0.9386 - auc: 0.9779 - val_loss: 0.5277 - val_accuracy: 0.8863 - val_auc: 0.9779\n", "Epoch 97/100\n", - " - 2s - loss: 0.1702 - accuracy: 0.9208 - auc: 0.9697 - val_loss: 0.4978 - val_accuracy: 0.8717 - val_auc: 0.9698\n", + " - 2s - loss: 0.1359 - accuracy: 0.9374 - auc: 0.9779 - val_loss: 0.5054 - val_accuracy: 0.8931 - val_auc: 0.9780\n", "Epoch 98/100\n", - " - 2s - loss: 0.1704 - accuracy: 0.9205 - auc: 0.9698 - val_loss: 0.4820 - val_accuracy: 0.8717 - val_auc: 0.9698\n", + " - 2s - loss: 0.1370 - accuracy: 0.9350 - auc: 0.9780 - val_loss: 0.5338 - val_accuracy: 0.8884 - val_auc: 0.9780\n", "Epoch 99/100\n", - " - 2s - loss: 0.1717 - accuracy: 0.9192 - auc: 0.9699 - val_loss: 0.4921 - val_accuracy: 0.8758 - val_auc: 0.9699\n", + " - 2s - loss: 0.1350 - accuracy: 0.9354 - auc: 0.9780 - val_loss: 0.4918 - val_accuracy: 0.8894 - val_auc: 0.9780\n", "Epoch 100/100\n", - " - 2s - loss: 0.1699 - accuracy: 0.9221 - auc: 0.9699 - val_loss: 0.5117 - val_accuracy: 0.8709 - val_auc: 0.9699\n" + " - 2s - loss: 0.1401 - accuracy: 0.9333 - auc: 0.9781 - val_loss: 0.5212 - val_accuracy: 0.8936 - val_auc: 0.9781\n" ] } ], "source": [ - "history_wo=model_wo.fit(X_train_wo_sensor_resample.drop(['proportion','label'],axis=1).values, y_train_wo_sensor_resample.values,\n", - " validation_data=(X_test_wo_sensor.drop(['proportion','label'],axis=1).values, y_test_wo_sensor),\n", + "history_wo=model_wo.fit(X_train_wo_sensor_resample.drop(['malwareNum','proportion','label'],axis=1).values, y_train_wo_sensor_resample.values,\n", + " validation_data=(X_test_wo_sensor.drop(['malwareNum','proportion','label'],axis=1).values, y_test_wo_sensor),\n", " epochs=100,batch_size=32,verbose=2)\n", "# history = model.fit(X_train, y_train, validation_data=(X_test, y_test),epochs=50,batch_size=32, shuffle=True)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1703,11 +1716,11 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ - "y_pred_wo_sensor = model_wo.predict(X_test_wo_sensor.drop(['proportion','label'],axis=1).values)\n", + "y_pred_wo_sensor = model_wo.predict(X_test_wo_sensor.drop(['malwareNum','proportion','label'],axis=1).values)\n", "y_pred_repack_benign_wo_sensor = model_wo.predict_proba(repackaged_benign_test_X_wo_sensors)\n", "# covid_y_pred_wo_sensor = model_wo.predict_proba(covid_test_X_wo_sensors)" ] @@ -1721,81 +1734,30 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC (including sensor features) 0.9302093317826264\n", - "AUC (not including sensor features) 0.9217231661142387\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (10, 6))\n", - "fpr, tpr, _ = roc_curve(y_test, y_pred_with_sensor)\n", - "fpr2, tpr2, _2 = roc_curve(y_test_wo_sensor, y_pred_wo_sensor)\n", - "plt.plot(fpr, tpr, lw=2, label='With sensor features')\n", - "plt.plot(fpr2, tpr2, lw=2, label='Without sensor features')\n", - "print('AUC (including sensor features)', roc_auc_score(y_test, y_pred_with_sensor))\n", - "print('AUC (not including sensor features)', roc_auc_score(y_test, y_pred_wo_sensor))\n", - "\n", - "plt.xlabel('False Positive Rate', fontsize = 13)\n", - "plt.ylabel('True Positive Rate', fontsize = 13)\n", - "plt.title('ROC Curve', fontsize = 15)\n", - "plt.legend(fontsize = 13)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "AUC (including sensor features) 0.9298030211513315\n", - "AUC (not including sensor features) 0.9166320120966037\n" + "AUC (including sensor features) 0.9573006766428322\n", + "AUC (not including sensor features) 0.9320767424504433\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 43, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1823,19 +1785,19 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([0.59328358, 0.95100354]),\n", - " array([0.79301746, 0.88080919]),\n", - " array([0.67876201, 0.91456145]),\n", - " array([ 401, 1829]))" + "(array([0.74770642, 0.94904891]),\n", + " array([0.81296758, 0.9270073 ]),\n", + " array([0.77897252, 0.93789862]),\n", + " array([ 401, 1507]))" ] }, - "execution_count": 47, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1846,19 +1808,19 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([0.61746362, 0.94053745]),\n", - " array([0.74064838, 0.89939858]),\n", - " array([0.67346939, 0.91950811]),\n", - " array([ 401, 1829]))" + "(array([0.73684211, 0.93758389]),\n", + " array([0.7680798, 0.9270073]),\n", + " array([0.75213675, 0.9322656 ]),\n", + " array([ 401, 1507]))" ] }, - "execution_count": 48, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1869,7 +1831,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1882,7 +1844,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1901,15 +1863,15 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Balanced Average Accuracy (including sensor features): 0.8369133208531432\n", - "Balanced Average Accuracy (not including sensor features): 0.8200234787552715\n" + "Balanced Average Accuracy (including sensor features): 0.8699874401587273\n", + "Balanced Average Accuracy (not including sensor features): 0.847543549884413\n" ] } ], @@ -1927,14 +1889,18 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "pairs = pd.read_csv('../../repackaging_pairs.txt')\n", "# Train test split\n", - "X_ = pd.read_csv('../data/Training_Data/Training_Dataset.csv')\n", - "y_ = pd.read_csv('../data/Training_Data/Labels.csv')\n", + "X_ = pd.read_csv('../data/Training_Data/Training_Dataset_with_threshold.csv')\n", + "y_ = pd.read_csv('../data/Training_Data/Labels_trainingset.csv')\n", + "\n", + "# X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", + "# covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", "\n", "X_train_, X_test_, y_train_, y_test_ = train_test_split(X_,y_['label'], \n", " test_size = 0.2, \n", @@ -1944,7 +1910,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1981,7 +1947,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1997,16 +1963,16 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1743" + "1585" ] }, - "execution_count": 53, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -2041,6 +2007,30 @@ "print(\"Accuracy for non-paired apps (not including sensor features):\", np.sum(non_paired_wo_sensor_accuracy)/len(non_paired_wo_sensor_accuracy))" ] }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for paired apps (including sensor features): 0.8571428571428571\n", + "Accuracy for non-paired apps (including sensor features): 0.9072696050372067\n", + "Accuracy for paired apps (not including sensor features): 0.8509316770186336\n", + "Accuracy for non-paired apps (not including sensor features): 0.8975386376645679\n" + ] + } + ], + "source": [ + "print(\"Accuracy for paired apps (including sensor features):\", np.sum(paired_w_sensor_accuracy)/len(paired_w_sensor_accuracy))\n", + "print(\"Accuracy for non-paired apps (including sensor features):\", np.sum(non_paired_w_sensor_accuracy)/len(non_paired_w_sensor_accuracy))\n", + "\n", + "print(\"Accuracy for paired apps (not including sensor features):\", np.sum(paired_wo_sensor_accuracy)/len(paired_wo_sensor_accuracy))\n", + "print(\"Accuracy for non-paired apps (not including sensor features):\", np.sum(non_paired_wo_sensor_accuracy)/len(non_paired_wo_sensor_accuracy))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2060,15 +2050,15 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy (including sensor features): 0.8075577326801959\n", - "Accuracy (without sensor features): 0.8684394681595521\n" + "Accuracy (including sensor features): 0.8848845346396081\n", + "Accuracy (without sensor features): 0.9688593421973408\n" ] } ], @@ -2669,12 +2659,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ - "def set_label(X,thres):\n", - " X_tp=X\n", + "def set_label_ratio(X,thres):\n", + " X_tp=X.copy()\n", " X_tp['label']=0\n", " X_tp.loc[X_tp['proportion']>=thres,'label']=1\n", " print(sum(X_tp['label'])/X_tp.shape[0])\n", @@ -2683,28 +2673,42 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "def set_label_count(X,thres):\n", + " X_tp=X.copy()\n", + " X_tp['label']=0\n", + " X_tp.loc[X_tp['malwareNum']>=thres,'label']=1\n", + " print(sum(X_tp['label'])/X_tp.shape[0])\n", + " return X_tp" + ] + }, + { + "cell_type": "code", + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ - "def build_model(X_train,X_test):\n", + "def build_model(X_train,X_test,drop_lst=[]):\n", " tf.keras.backend.clear_session()\n", " model = keras.Sequential()\n", " model = Sequential()\n", - " model.add(Dense(64, input_dim=X_train.shape[1]-2, activation='relu'))\n", + " model.add(Dense(32, input_dim=X_train.shape[1]-2, activation='relu'))\n", " model.add(Dropout(0.4))\n", - " model.add(Dense(16, activation='relu'))\n", + " model.add(Dense(48, activation='relu'))\n", " model.add(Dropout(0.4))\n", " # model.add(Dense(128, activation='relu'))\n", " model.add(Dense(1, activation='sigmoid'))\n", " # Compile model\n", " model.compile(loss='binary_crossentropy', optimizer=adam(lr=0.0001), metrics=['accuracy',tf.keras.metrics.AUC()])\n", - " history=model.fit(X_train.drop(['proportion','label'],axis=1).values, X_train['label'].values,\n", + " history=model.fit(X_train.drop(drop_lst,axis=1).values, X_train['label'].values,\n", " # validation_split=0.2,\n", - " validation_data=(X_test.drop(['proportion','label'],axis=1).values, X_test['label']),\n", + " validation_data=(X_test.drop(drop_lst,axis=1).values, X_test['label']),\n", " verbose=0,\n", " epochs=100,batch_size=32)\n", - " y_pred=model.predict(X_test.drop(['proportion','label'],axis=1))\n", + " y_pred=model.predict(X_test.drop(drop_lst,axis=1))\n", " return model,y_pred\n", " # history = model.fit(X_train, y_train, validation_split=0.2,epochs=50,batch_size=32, shuffle=True)\n", " " @@ -2712,1223 +2716,283 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ - "def compute_metric_thres(X_train,X_test,thres):\n", - " X_train_tp=set_label(X_train,thres)\n", - " X_test_tp=set_label(X_test,thres)\n", + "def compute_metric_thres(X_train,X_test,thres,drop_lst=[],mode=1):\n", + " if mode==1:\n", + " X_train_tp=set_label_ratio(X_train,thres)\n", + " X_test_tp=set_label_ratio(X_test,thres)\n", + " else:\n", + " X_train_tp=set_label_count(X_train,thres)\n", + " X_test_tp=set_label_count(X_test,thres)\n", + " \n", " X_train_resample_tp,y_train_resample_tp=resample(X_train_tp)\n", - " print(X_train_resample_tp.columns)\n", - " model_with_sensor,y_pred_with_sensor = build_model(X_train_resample_tp,X_test_tp)\n", + "# print(X_train_resample_tp.columns)\n", + " model_with_sensor,y_pred_with_sensor = build_model(X_train_resample_tp,X_test_tp,drop_lst)\n", " score=balanced_accuracy_score(X_test_tp['label'], np.round(y_pred_with_sensor))\n", "# print('Balanced Average Accuracy (including sensor features):', score)\n", " metrics=precision_recall_fscore_support(X_test_tp['label'], np.round(y_pred_with_sensor))\n", " recall=metrics[1]\n", " prcision=metrics[0]\n", " print('balanced acc=%s recall_benign=%s recall_malware=%s'%(str(score),str(recall[0]),str(recall[1])))\n", - " return score,recall[0],recall[1],prcision[0],prcision[1]\n" + " return score,recall[0],recall[1],prcision[0],prcision[1]" ] }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, + "execution_count": 66, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "------thres= 0.01\n" + "------thres= 0.01\n", + "0.7897771952817825\n", + "0.789832285115304\n", + "balanced acc=0.879812744184661 recall_benign=0.8478802992518704 recall_malware=0.9117451891174518\n", + "------thres= 0.03\n", + "0.7896461336828309\n", + "0.789308176100629\n", + "balanced acc=0.8896701750213079 recall_benign=0.8756218905472637 recall_malware=0.9037184594953519\n", + "------thres= 0.049999999999999996\n", + "0.7870249017038008\n", + "0.7861635220125787\n", + "balanced acc=0.8699803921568627 recall_benign=0.8186274509803921 recall_malware=0.9213333333333333\n", + "------thres= 0.06999999999999999\n", + "0.7815203145478374\n", + "0.7809224318658281\n", + "balanced acc=0.8756045085257378 recall_benign=0.8444976076555024 recall_malware=0.9067114093959732\n", + "------thres= 0.08999999999999998\n", + "0.7745740498034076\n", + "0.7772536687631028\n", + "balanced acc=0.8733148228947681 recall_benign=0.8329411764705882 recall_malware=0.9136884693189481\n", + "------thres= 0.10999999999999997\n", + "0.7623853211009174\n", + "0.760482180293501\n", + "balanced acc=0.8583117053507201 recall_benign=0.8227571115973742 recall_malware=0.8938662991040661\n", + "------thres= 0.12999999999999998\n", + "0.7428571428571429\n", + "0.7389937106918238\n", + "balanced acc=0.8394984192087499 recall_benign=0.7811244979919679 recall_malware=0.8978723404255319\n", + "------thres= 0.15\n", + "0.7193971166448231\n", + "0.720125786163522\n", + "balanced acc=0.8311867261992378 recall_benign=0.7846441947565543 recall_malware=0.8777292576419214\n", + "------thres= 0.16999999999999998\n", + "0.6657929226736566\n", + "0.6645702306079665\n", + "balanced acc=0.8242754337539432 recall_benign=0.803125 recall_malware=0.8454258675078864\n", + "------thres= 0.18999999999999997\n", + "0.608781127129751\n", + "0.6158280922431866\n", + "balanced acc=0.8174990566311573 recall_benign=0.7830832196452933 recall_malware=0.8519148936170213\n", + "------thres= 0.20999999999999996\n", + "0.5636959370904325\n", + "0.5718029350104822\n", + "balanced acc=0.8229185715551857 recall_benign=0.7649938800489596 recall_malware=0.8808432630614116\n", + "------thres= 0.22999999999999998\n", + "0.5174311926605505\n", + "0.5178197064989518\n", + "balanced acc=0.8070014082027812 recall_benign=0.7152173913043478 recall_malware=0.8987854251012146\n", + "------thres= 0.24999999999999997\n", + "0.4748361730013106\n", + "0.470125786163522\n", + "balanced acc=0.7861880518311947 recall_benign=0.6805143422354105 recall_malware=0.8918617614269788\n", + "------thres= 0.26999999999999996\n", + "0.3871559633027523\n", + "0.3731656184486373\n", + "balanced acc=0.7548053436548796 recall_benign=0.5994983277591973 recall_malware=0.9101123595505618\n", + "------thres= 0.29\n", + "0.2916120576671035\n", + "0.28354297693920333\n", + "balanced acc=0.741527583777637 recall_benign=0.8046817849305048 recall_malware=0.678373382624769\n", + "------thres= 0.30999999999999994\n", + "0.21874180865006554\n", + "0.21016771488469602\n", + "balanced acc=0.7707779324085274 recall_benign=0.8108825481088254 recall_malware=0.7306733167082294\n", + "------thres= 0.32999999999999996\n", + "0.17116644823066843\n", + "0.15828092243186584\n", + "balanced acc=0.7967637914113466 recall_benign=0.8518057285180572 recall_malware=0.7417218543046358\n", + "------thres= 0.35\n", + "0.1436435124508519\n", + "0.1278825995807128\n", + "balanced acc=0.8038402427490543 recall_benign=0.8617788461538461 recall_malware=0.7459016393442623\n", + "------thres= 0.36999999999999994\n", + "0.11651376146788991\n", + "0.1090146750524109\n", + "balanced acc=0.8074943438914027 recall_benign=0.8794117647058823 recall_malware=0.7355769230769231\n", + "------thres= 0.38999999999999996\n", + "0.09541284403669725\n", + "0.09014675052410902\n", + "balanced acc=0.7929080484406816 recall_benign=0.8997695852534562 recall_malware=0.686046511627907\n" ] - }, + } + ], + "source": [ + "import collections\n", + "pd_metric=collections.defaultdict(list)\n", + "for thres in list(np.arange(0.01,0.4,0.02)):\n", + " print('------thres=',thres)\n", + " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train.drop('malwareNum',axis=1),\n", + " X_test.drop('malwareNum',axis=1),thres,['proportion','label'],1)\n", + " pd_metric['thres'].append(thres)\n", + " pd_metric['balanced_accuracy'].append(score)\n", + " pd_metric['benign_recall'].append(recall1)\n", + " pd_metric['malware_recall'].append(recall2)\n", + " pd_metric['benign_precision'].append(precision1)\n", + " pd_metric['malware_precision'].append(precision2)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(pd_metric).to_csv('../../dnn_threshold.csv',index=False)\n", + "df=pd.DataFrame(pd_metric)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8087707492148946\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", + "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", + "plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", + "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", + "# plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8103139013452915\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8417475512225244 recall_benign=0.7919621749408984 recall_malware=0.8915329275041505\n", - "------thres= 0.03\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7994616419919246\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8031390134529148\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8404093359737183 recall_benign=0.7813211845102506 recall_malware=0.8994974874371859\n", - "------thres= 0.049999999999999996\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7520188425302826\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7457399103139013\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8526483130612215 recall_benign=0.7918871252204586 recall_malware=0.9134095009019844\n", - "------thres= 0.06999999999999999\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7239793629430238\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the 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trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.4728577837595334\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.4780269058295964\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.84138201711121 recall_benign=0.756872852233677 recall_malware=0.925891181988743\n", - "------thres= 0.24999999999999997\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.4329295648272768\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.437219730941704\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 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"text": [ - "0.3566621803499327\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.34798206278026905\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - 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DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2711978465679677\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] 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- "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20134529147982064\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " 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"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1320098698968147\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1078959174517721\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.10582959641255606\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8448693538241844 recall_benign=0.8846539618856569 recall_malware=0.8050847457627118\n", - "------thres= 0.38999999999999996\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.08770749214894571\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", + "plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", + "\n", + "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", + "\n", + "# plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", + "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.08475336322869956\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8397947888393749 recall_benign=0.8912297893189612 recall_malware=0.7883597883597884\n" + "------thres= 17\n", + "0.27706422018348625\n", + "0.2688679245283019\n", + "balanced acc=0.7474816072439163 recall_benign=0.810752688172043 recall_malware=0.6842105263157895\n", + "------thres= 18\n", + "0.2182175622542595\n", + "0.2112159329140461\n", + "balanced acc=0.7822428134506154 recall_benign=0.812624584717608 recall_malware=0.7518610421836228\n", + "------thres= 19\n", + "0.1817824377457405\n", + "0.17033542976939203\n", + "balanced acc=0.7950755624665922 recall_benign=0.8332280480101074 recall_malware=0.7569230769230769\n", + "------thres= 20\n", + "0.15176933158584535\n", + "0.139937106918239\n", + "balanced acc=0.7912549897637094 recall_benign=0.8671541742839732 recall_malware=0.7153558052434457\n", + "------thres= 21\n", + "0.13171690694626476\n", + "0.11740041928721175\n", + "balanced acc=0.8113282575500509 recall_benign=0.8592636579572447 recall_malware=0.7633928571428571\n", + "------thres= 22\n", + "0.11507208387942333\n", + "0.10377358490566038\n", + "balanced acc=0.8097022860180756 recall_benign=0.8719298245614036 recall_malware=0.7474747474747475\n", + "------thres= 23\n", + "0.0981651376146789\n", + "0.08962264150943396\n", + "balanced acc=0.8169408841620458 recall_benign=0.8911917098445595 recall_malware=0.7426900584795322\n", + "------thres= 24\n", + "0.08256880733944955\n", + "0.07651991614255765\n", + "balanced acc=0.821358045807224 recall_benign=0.8961407491486947 recall_malware=0.7465753424657534\n", + "------thres= 25\n", + "0.06828309305373526\n", + "0.0660377358490566\n", + "balanced acc=0.8448372615039281 recall_benign=0.9118967452300786 recall_malware=0.7777777777777778\n" ] } ], "source": [ "import collections\n", - "pd_metric=collections.defaultdict(list)\n", - "for thres in list(np.arange(0.01,0.4,0.02)):\n", + "# pd_metric=collections.defaultdict(list)\n", + "for thres in list(np.arange(17,26,1)):\n", " print('------thres=',thres)\n", - " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train,X_test,thres)\n", + " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train.drop('proportion',axis=1),\n", + " X_test.drop('proportion',axis=1),thres,['malwareNum','label'],2)\n", " pd_metric['thres'].append(thres)\n", " pd_metric['balanced_accuracy'].append(score)\n", " pd_metric['benign_recall'].append(recall1)\n", @@ -3939,324 +3003,100 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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thresbalanced_accuracybenign_recallmalware_recallbenign_precisionmalware_precision
00.010.8417480.7919620.8915330.6308850.948205
10.030.8404090.7813210.8994970.6558320.943761
20.050.8526480.7918870.9134100.7571670.927917
30.070.8479900.7776000.9183800.7876820.913825
40.090.8550420.7990870.9109980.7894740.915655
50.110.8590030.8052710.9127340.8029200.913916
60.130.8530250.7880060.9180440.8200290.901363
70.150.8491140.7745740.9236540.8406830.887361
80.170.8251430.7767650.8735210.7995310.857662
90.190.8430910.8198380.8663450.8299180.858054
100.210.8486590.8037560.8935620.8734690.832800
110.230.8413820.7568730.9258910.9177080.777165
120.250.8277840.7386450.9169230.9196430.731588
130.270.7935240.6540580.9329900.9481560.590057
140.290.7794580.6355830.9233330.9574860.482578
150.310.8166590.8293090.8040090.9437700.542857
160.330.8297560.8628080.7967030.9560570.531136
170.350.8313950.8634480.7993420.9646170.480237
180.370.8448690.8846540.8050850.9745860.452381
190.390.8397950.8912300.7883600.9784830.401617
\n", - "
" - ], "text/plain": [ - " thres balanced_accuracy benign_recall malware_recall benign_precision \\\n", - "0 0.01 0.841748 0.791962 0.891533 0.630885 \n", - "1 0.03 0.840409 0.781321 0.899497 0.655832 \n", - "2 0.05 0.852648 0.791887 0.913410 0.757167 \n", - "3 0.07 0.847990 0.777600 0.918380 0.787682 \n", - "4 0.09 0.855042 0.799087 0.910998 0.789474 \n", - "5 0.11 0.859003 0.805271 0.912734 0.802920 \n", - "6 0.13 0.853025 0.788006 0.918044 0.820029 \n", - "7 0.15 0.849114 0.774574 0.923654 0.840683 \n", - "8 0.17 0.825143 0.776765 0.873521 0.799531 \n", - "9 0.19 0.843091 0.819838 0.866345 0.829918 \n", - "10 0.21 0.848659 0.803756 0.893562 0.873469 \n", - "11 0.23 0.841382 0.756873 0.925891 0.917708 \n", - "12 0.25 0.827784 0.738645 0.916923 0.919643 \n", - "13 0.27 0.793524 0.654058 0.932990 0.948156 \n", - "14 0.29 0.779458 0.635583 0.923333 0.957486 \n", - "15 0.31 0.816659 0.829309 0.804009 0.943770 \n", - "16 0.33 0.829756 0.862808 0.796703 0.956057 \n", - "17 0.35 0.831395 0.863448 0.799342 0.964617 \n", - "18 0.37 0.844869 0.884654 0.805085 0.974586 \n", - "19 0.39 0.839795 0.891230 0.788360 0.978483 \n", - "\n", - " malware_precision \n", - "0 0.948205 \n", - "1 0.943761 \n", - "2 0.927917 \n", - "3 0.913825 \n", - "4 0.915655 \n", - "5 0.913916 \n", - "6 0.901363 \n", - "7 0.887361 \n", - "8 0.857662 \n", - "9 0.858054 \n", - "10 0.832800 \n", - "11 0.777165 \n", - "12 0.731588 \n", - "13 0.590057 \n", - "14 0.482578 \n", - "15 0.542857 \n", - "16 0.531136 \n", - "17 0.480237 \n", - "18 0.452381 \n", - "19 0.401617 " + "defaultdict(list,\n", + " {'thres': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],\n", + " 'balanced_accuracy': [0.8823969251039043,\n", + " 0.8680678629449121,\n", + " 0.8448637316561844,\n", + " 0.83506646951034,\n", + " 0.8257642844959918,\n", + " 0.8109771181199752,\n", + " 0.8041378182412909,\n", + " 0.8187374822286563,\n", + " 0.8302835749419288,\n", + " 0.7993311386032502,\n", + " 0.7707190505731385,\n", + " 0.7451151328605938],\n", + " 'benign_recall': [0.8568019093078759,\n", + " 0.831081081081081,\n", + " 0.7756813417190775,\n", + " 0.7833001988071571,\n", + " 0.7729831144465291,\n", + " 0.7568027210884354,\n", + " 0.7616892911010558,\n", + " 0.7804551539491299,\n", + " 0.7723970944309927,\n", + " 0.6973262032085561,\n", + " 0.6310772163965681,\n", + " 0.603035143769968],\n", + " 'malware_recall': [0.9079919408999328,\n", + " 0.9050546448087432,\n", + " 0.9140461215932913,\n", + " 0.8868327402135231,\n", + " 0.8785454545454545,\n", + " 0.8651515151515151,\n", + " 0.8465863453815261,\n", + " 0.8570198105081827,\n", + " 0.8881700554528651,\n", + " 0.9013360739979445,\n", + " 0.910360884749709,\n", + " 0.8871951219512195],\n", + " 'benign_precision': [0.7237903225806451,\n", + " 0.7263779527559056,\n", + " 0.7505070993914807,\n", + " 0.7124773960216998,\n", + " 0.7115716753022453,\n", + " 0.7142857142857143,\n", + " 0.7255747126436781,\n", + " 0.7783711615487316,\n", + " 0.8405797101449275,\n", + " 0.8716577540106952,\n", + " 0.8958051420838972,\n", + " 0.9107358262967431],\n", + " 'malware_precision': [0.9575070821529745,\n", + " 0.9464285714285714,\n", + " 0.9243816254416961,\n", + " 0.9195571955719557,\n", + " 0.90895410082769,\n", + " 0.888715953307393,\n", + " 0.8696369636963697,\n", + " 0.8584987057808455,\n", + " 0.8363794604003482,\n", + " 0.7560344827586207,\n", + " 0.6689478186484175,\n", + " 0.5393883225208527]})" ] }, - "execution_count": 51, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.DataFrame(pd_metric)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(pd_metric).to_csv('../../dnn_threshold.csv',index=False)" + "pd_metric" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "pd.DataFrame(pd_metric).to_csv('../../dnn_threshold_count.csv',index=False)\n", "df=pd.DataFrame(pd_metric)" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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CL6Xk7KWzbE/azvak7ew5v4fc4lwshAXBnsE8GvIo/Vv3J8gzyOiCX06TEn5F88DCQvDEkA488+N+1h0+b5INTaSUvPxbLBuPpfL2uGCGdrv+ZunNES8XO3xc7IhJyDS1KQ1OVmEWu8/vZnvSdnYk7SAxJxEAXydfRgeMpl/rfkS0isDFxsUk9inhVzRKbglpzUd/HWfuxjhGdPdp8PxJczbE8eOeczx1Y0ezmmQ2N4J8XZtNiubEnESWn1jOjuQdxKZddt9E+EQwpfsU+rfuTxuXNjU31AAo4Vc0SiwtBI8P7sD0X2L453gqg7t4NVjfP+85x0frjzM+zI/nhnVusH4bIyF+rmw4eoFLBcVNet5tf8p+nvr7KbKLsk3mvqkN5meRQqEn43r6MXv9CeZtjGsw4d90LIUXlscwsJMns8YHq0ytNRDsd3mCt2/7FqY2xyisP7OeGVtm4OPow5Kbl+DvYv53gKaPhVMo6oiNlQWPDurAnvgMdp26aPT+YhKyeGLJPrr6ODP/vl5Ym0EoqblTMcHbROP5vzv8Hc9teo6uHl1ZPGpxoxB9UMKvaOTc3bsNnk62zN0YZ9R+zqXnMWXRHtwdbFj4QG+cVFy6Xng62dLa1a7JreDVSR3v7nmXd/a8w43+N/Ll8C9xt3M3tVl6o4Rf0aixs7bk4YEBbDmRxoFzmUbpIyO3iMlf76a4VMc3D/ZW223WkmA/1yYl/IWlhfznn/+w+PBi7u12Lx8M+gA7q8b1m1DCr2j03Ne3La721kYZ9RcUl/LQN3tIyMznq8nhzTrxWl0J8XPjdFouWfmNYy+F65FVmMXUdVNZd2Ydz4c/z/Te07G0MK+cUfqghF/R6HGyteKB/u346/AFjp433D6vpTrJ0z9EE30uk9l39yC8XfNdhFQfgsr8/Ica+ag/4VIC962+j5i0GN4b9B6Tu09utJP7SvgVTYIpA9rhaGPJgwv38OYfh9l+Mo3iUl2d25NS8sbKQ6w7fIFXbwlkVHDDLxJrKlRewdtYOZR2iPtW30d6QTpfDP+Cke1GmtqkeqFmqBRNAjcHG+bdG8bCbfEs3nGGr7aextnWihu6tGRoVy8Gd/HCoxZ7Lny++RTf7jjD1BvaM2WAeeb+byx4ONrg527faBdybU7YzPP/PI+7rTtfj/ia9m7tTW1SvVHCr2gyDO6iCXxuYQlb49L4+0gKG46msOpgMhYCwvzdubGbF0O7etPZ26na2/Tf9ycya81RxoS2ZobK/2QQghtpiuZlx5fxfzv/j87unfn0pk/xtPc0tUkGQQm/osnhaGvFiO4+jOjug06nbbW54WgKfx+9wLtrj/Hu2mP4udsztKsXN3bzpk+AB3bW2gTdtrg0nl96gL7tPXj/zhCTJYBragT7ubIm9jxZecWNYk8MKSVz989lwcEFRPpG8sGgD3CwdjC1WQZDCb+iSWNhIQht40ZoGzeeG9aZ81kFbDyWwoYjF/gp6hzf7DiDg40lkR09iQjwYPb6E7T3dOLzSeFmt8NXYybE1w3Q/PyRncx71FxcWszrO15nxckVjO80npf7vmyWaRfqQ9P6NgpFDfi42jEhwp8JEf4UFJey4+RFNhy9wN9HUlh3+AI+LnYsnNIbV3vzH5U2JoJ8tSyUBxMzzVr4LxVd4rlNz7EzeSdP9niSqSFTG23kzvVQwq9otthZWzKkqxdDunohx0qOXbiEu4MN3mqBlsFxc7DB38PBJH5+ndRRVFpEsa64yuciXRFFpUUUlBTw8b6POZV5irci3+LWDrc2uK0NhRJ+hQJtZ6+uPqbJjd5cCPZzNdrqaoCFsQv56dhPFJYWXhb30mJKZInebThaOzLvpnn0b93faHaaA0r4FQpFgxDi68qqg8mk5xbVKrRWH7YnbefDvR8S5hVGgGsANpY2WFtYY2Npg42FDdaW1lc821hqr60tLr+3sbShjXObJhO5cz2U8CtASshOhNRjYO8OPiFgqX4aCsNSeSHXoM4tDdZuekE6L219iQ6uHfh82OeNLm+OKVD/3c0JnQ6yzmoCn3r0yueinMvlbJyhbT9oOwDaDYRWoepCoKg33cuEP9aAwi+l5JVtr5BdmM1nN32mRF9P1H9zU0RXChnxZcJ+pJLAH4eS/MvlnHygZRfoca/27NkZclMgfqv2OLFOK2fjBP59yy4EkdC6J1iqqBdF7XC1tybA05GDBtyD94ejP7A5YTMzImbQxaOLwdpt6ijhbwoUZMHJv+HEekg+AGnHobTw8ucufpqwh0dqzy27QsvOmlunKoLGa885ZReBM9u05w1vaMetHaFNhHYRaBcJrcPAyrA+W0XTJMjXlb3x6QZp63jGcT6I+oAb/G5gYteJBmmzuaCX8AshRgKzAUvgSynlrKs+dwW+A/zL2nxfSrmw7LN44BJQCpRIKcMNZr25UZwPwtL4IiilNoo/8Sec+AvO7gBdCdi5gV9v6DC4TNy7aqN4uzpGqzh5QdDt2gMgJ1W7CJRfCP5+UztuZV92IRgIvmHaxcXFF5pg/LOifoT4urLyQBJpOYV4OtnWuZ2CkgKmb56Oi60Lbw54s0nG2huTGoVfCGEJzAOGAQnAHiHECinl4UrFpgGHpZRjhBAtgWNCiCVSyqKyz4dIKdMMbbzZkJcO2+fArgWgKwavbtoEaatQ7eHdHWwc69dHcb4mtsf/1AQ/86x23DsI+j8FnUZoom9MX7xTS+h+m/YAyL145YVg4/9dLmvjDJ6dLt9dlF+E3NtBI8xfrjAMwX6XJ3iH1GOf5Pej3icuM47Pb/ocDzuVLru26KMSEUCclPIUgBDiR2AsUFn4JeAstMuuE5AO6B8821jJz4Ad82DnZ9rkaPdx4NZGc7ccXQXRi7VywgJadCq7EJRdEHyCq3e1lJN5TvOzn1gHp/7R/PPWDhAwCCKfhU7DwdXP+N+zOhxbQOCt2gO0C+CFQ5B2rGxe4Zjmgjrw/eU6lrZlF4Quly8GLbuCR3vlLmoGdG+t3X3GJNRd+Dee3chPx35icuBk+vs27Xh7Y6GP8PsC5yq9TwD6XFVmLrACSAKcgbullOXJ0CWwTgghgc+llAuq6kQIMRWYCuDvb+YbFhdkwc75mugXZkPgWBg0A7wDL5eRErIS4PxB7UKQfFAbFcf8fLmMW9tKF4JQ8AnSJmVPrIPj6yDl0OVyYZO0UX27SLA208gFBw8IGKg9KpOfqc07VEQSHYOEPRD7y+UywhJadICwydD/yQY1u95ICRmn4dxusLKDDkPAztXUVpklznbWtG/pyME6ruBNyUvh1e2v0s2jG0+HPW1g65oP+gh/Vc4zedX7EcB+4EagA/CXEGKLlDIbGCClTBJCeJUdPyql3HxNg9oFYQFAeHj41e2bB4WXYNdnsP0TTfy73gKDZ2ij96sRQhv9u7WBrqMvH89JhfNlF4LkA9qF4cjKK+taWIF/Pxj2JnQeoY2KG7MP095NmwNoE3Hl8aJcSDuhXQjSjsHZnbDuJe1iOvgF8/3OpcXa3+/sDji3E87u0qKhyrGwhnYDoPMo6DJSc28pKgjxdWXnqdpP8Oqkjhe3vkhhaSHv3PAONpbqDrGu6CP8CUCbSu/90Eb2lZkCzJJSSiBOCHEa6ArsllImAUgpU4QQy9FcR9cIv1lTmAN7voBtcyA/XfuHHjwDWveofVtOLaHjTdqjnIIsOB8LF2LBybv5jBhtHLVzWH4edTpY8RT88442ih7yonmIf34GnNtzWeQT914Oi3Vrq/292vTRQl4LsuH4Gji2FtZO1x5egdB5JHQZBb69mv0cR7CfG7/tTyIlu6BWG9d/c+gbdiXv4o3+bxDgqjbHqQ/6CP8eoJMQIgBIBO4Bro6dOgsMBbYIIbyBLsApIYQjYCGlvFT2ejgw02DWG5uiPIj6CrZ+DHlp0HGYNhL162XYfuxctRFiuwGGbbexYWEBt36iif3mdwEJQ15qWPGXUnO3ndul3YGc2wUpRzRbhKXmmuv1APj3gTZ9waWKLRnb9oNhM+HiSTi+Fo6tgW2zYeuH4NhSc9l1GQnth4CtU8N9NzOh8greoXoK/6G0Q8zZN4dhbYcxruM4Y5pXN9JOwPrXtcFbq1DtAt86TBvU2Dqb2rprqFH4pZQlQogngT/Rwjm/llIeEkI8Vvb5Z8CbwCIhRAyaa2i6lDJNCNEeWF4WamUFfC+lXGuk72I4igtg70LY8qF2C99+iDb6vNpVoTA8FhYwZk6Z+L+nCfGNLxtf/IvzYcNMbd4h54J2zNZFi5Tqfrsm9L69ahed1aID9JumPfIzIG6DdhE4uhL2f6dNdAfcoF0EOo8CV1/jfDczo3trF4QoE/5u3jWWzyvOY/qW6Xg6ePJav9fMK3Qz9yL8MwuivtbCmtsPgqT9cPj3sgJCC17w7aWFOvv20qL8TLwAUmjeGfMiPDxcRkVF1b7ivL7as52LdpWteLiUPZyvfNi5XvnewlqLxNnyAVxK1uLSh7wIbVXkQIOj08Ef/4J930DkczD0VeOJf+pxWPqANpkeeJs2Od2mrxaWawy3TGmxNj9wbC0cW61NDIMWAhw6AXo/BFZ1j3FvDAz78B/8PRz46oHeNZZ9Zdsr/B73O1+P+JpwHzNZBlRSqM33bf5Ai+jr9YDmDXAqS0WRmwZJ0ZpbsPyRd1H7zMpO+1v79rp8QfBoX+/ftxBir77rpJrWyl2/Xpq/vPCSNsLKOKO9LrwExbn6t+PfD25foI3GFKbBwgJu+VgLhd36ISBh6GuGF/8DP8Ifz2mRUvcug07DDNt+VVhaa7+tgBtgxFtaxNOxNVoI8J8vwO7PNVdRt1vNY47DCAT7urIlLg0p5XVH8GtPr+W3uN+YGjLVPERfSjj0q+bWyTyrue2GzQSvq/ZmdvTUfkvlvycpIfNM2UVgn/a8dxHsmq99bud2+Y5g8Iva79+INK0R//UoLYGiS5cvBAXZZa8rP+doo/v2g5vsP1yjQ6eD1f/WbqUH/Atuet0wf5uiXFj9H9i/BPz7wx1fgUvr+rdbX+I2wJ8vaTmW/PtrFwbfMFNbZXAWbjvNGysPs/OFofi4Vu3nT8pJ4o4Vd9DerT2LRi4y/faH53bDny9qocjeQTD8/7SJ/bpSWqL9ncsvBIn7oKQAnqqb9jXfEf/1sLTSFkzVtGhKYV5YWMDNHwACtn0MUqeNsOoj/ilHNNdO6jG44T/aGgxzyT7acai2QC96MWx8C74Yorl/bnylSc0BhFRawVuV8JfoSpixZQY6dMwaOMu0op9+WhvhH/5NS2x461zoMbH+bkBLKy0U3CcYek3WjpU2zLpXM/m1KxTXwcICRn+gif32OYDU1jjUVvyl1AR19X+1OZ1Jy+s3YjMWllYQPkVLlrf1Q9jxKRz6DQY8DQOeqX/6DzMgsJUrFgJiEjIZFnjtBO8XB78gOiWaWQNn4edsotXp+ZlagMHuBdramkEztPQoxozEaqABiBJ+ReNACLj5fUBoC+ik1G619RX/wkuaLz/mZ823fvuX4FxzRIlJsXPRXFu9pmgjzn/egb3faBPdoROM7gc2JvY2lnTycuZg4rUreKNTovns4GeMaT+G0e1HV1HbyJQWa67FTbO0ucIe98KNL5mHK9BAKOFXNB6EgJvf0yZ8d8zVxH/EWzWL//kYzbWTfkpbFzDw341rEZV7W7hzIfR5TJv8/f0JbQJ4xNtaCo9GSrCfK5uOpVwxwZtdlM2MzTNo7diaF/u82LAGSalFWf31KlyM01xuw/9PW7vRxFDCr2hcCAGj3tGed84DpCaAVYm/lNrIbe0L2tzO/SuuzSPUmPDvAw+t16JK/noNFo3W0oYMm6mtGWhkhPi5smxvAslZBbR2s0dKyZs73iQlL4VvR32Lk00dXSo6nRasUZCpuWvyM8peZ2jvK7+u+CxLe110SUuRMvFnLQliEw3yUMKvaHwIASNnAQJ2fqoJ/Mj/XflPWpAFK5+BQ8uhw1AY9/nlGOvGjIUFBN+h5X/aMQ+2fgTz+kDEVBj0n0YVvBBUtoL3YEIWrd3s+ePUH6yNX8szYc8Q3LKK/FfXIy1O2x/i9D/a374iR2QVWNqWBXq4aWGULn5alI69u7a4KuRuky+wMjZK+BWNEyHKxN7i8sh/5CzteFI0LJ2ixVkPfU0LA23E/vAqsbaHG56HnpO0fRB2fqqlvx71HoTcaWrr9CKwlQuWFoLYxCxGBvmw6tQq2rm0Y0r3Kfo3kpOi+eL3LtLOSdDtWr4rO7fL4m7vXva+7LW1vVG+T2NCCb+i8SLEZR//jrnaKK9FR1j3spYTZ8pqLXFaU8bZW8tvFDFVm7z+7TFtv4O6JBBsYOysLensfXmCNzEnkU7unbDUZ/6l8BJsn6tN9JcWQviDMGh607irawCU8CsaN0JoE3CgiT9omTBvm6/tD9Bc8AmGiT/B/AHw6yMw9R+wcTC1VTUS7OvCX4cvoNPpSM5NZnCbwdevUFqsje7/eQdyU7UUG0NfbZRzHKZECb+i8VMu/s6ttBw3vR9uspNy18XBA8bNh2/HapEpo983tUU1Euznxs9RCcScT6SwtJDWTtWETEqpJT7bMBPST0LbATDhR/AzgzQOjRAl/IqmgRCNb+cuY9B+MPR7Urv76TQcOg83tUXXJaRsgnfH2RMA+DpVsTo5fpt2IUuMgpbdYMJP2gZFzfHibiCa2IyXQlF3zueeJyUvpeaC5s6Nr4BXd/h9mpYl0ozp2soZa0vBgfNahtLWjpVG/ClH4Pu7YdHNkJ2kpUp4fJuWxlqJfr1Qwq9QAPkl+UxaM4lblt/CkiNL0F0vHNDcsbaD8V9o8ekrntbcJGaKrZU2wRuXfhZAc/VkJ8HvT8L8/nBmu+bDf2qvtu90Y1p4Z8Yo4VcogMWHF3M+9zxd3Lswa/csHvzzQc5lnzO1WXXHu7uW7uHYKtj3ramtuS4hfq6cz03GzcYVh3/egzlhWrrsPo/B0/u1ldaNYKK6MaGEX9HsSctP48uYL7nJ/ya+HfUtM/vP5Hj6ccavHN+4R/99HtfSDqydoW0DaaYE+7pRYnGRVgW5WlK6brdoqYlH/g8cW5javCaJEn5Fs2du9FyKdcU82+tZhBCM6zSOX8f+Si/vXo179G9hoYW1WtpoIZ6lxaa2qEqCfV1xs07ALz8bxs6D8V+CeztTm9WkUcKvaNYczzjO8rjl3NPlHvxd/CuO+zj68OnQT5nZfybH0o813tG/qy+M+Vjb6GPze6a2pko62aZTbJ2Lo0VLLROmwugo4Vc0az6I+gAnayceC33sms/KR//Lxy4nzDuMWbtn8dCfDzW+0X/3cVoa583vabtImRNSkrPmGQotBIcsh6honQZCCb+i2bI1cSvbk7bzWOhjuNq6VlvOx9GH+UPnM7P/TI6mH22co/9R74Krn+byKbxkamsuc+BHkhJ3AHAi3QudznwjkJoSSvgVzZISXQkfRH2Av7M/93S5p8by1Y7+LzWS0b+dC4xboCWuWzvD1NZo5KTA2hkkeXcDIDfPhTPpeSY2qnmghF/RLFket5y4zDie7fUs1rVIwXvN6H/FeL4/8n3jGP237QeRz0H0d3B4hamt0Ta7L84jKfAWAHTFbsRUsSOXwvAo4VeQW5zL5oTNzN43m7/O/GVqc4xOTlEOc6PnEuYVxlD/obWuf8Xo3yuM/+3+X+MZ/Q+eAa17wsqnITvZdHYc+UPbvHzQdJIowtXGFRsLB2ISMk1nUx2RUnIuPY9f9iawOiYZacYL5srRK1ePEGIkMBuwBL6UUs666nNX4DvAv6zN96WUC/Wpq2h4CkoK2J+6n93Ju9l9fjexabGUylIEAonklva38FKfl+q+A5KZ83Xs16QXpPPp0E8rtvyrCz6OPsy/aT7L45bz3p73GL9iPK/3e52b299sQGsNjKU13P4FfDZQ28Lx3l8afq+C/ExY9W/wDoYBz5C48WlaO7WmRSsX1sSep6OXEzd29aals23D2qUnOp3keMol9pxOZ3d8BntOp3M+u6Di88FdWvLu+BC8XOxMaOX1ETVdnYQQlsBxYBiQAOwBJkgpD1cq8yLgKqWcLoRoCRwDfIDSmupWRXh4uIyKiqrzl1JcSXFpMTFpMew6v4vdybs5kHqAYl0xlsKSIM8gInwiiGgVQbBnMN8e/pbPD3yOj6MPswbOoodXD1Obb1DO557nluW3cFPbm5g10HBjkPO55/n3P//mVOYp1ty+Bjc7N4O1bRT2fAWrntMmffs82rB9r3hKczc98je07snY38bS3rU9o7ym88bKwyRm5iME9Gjjxk3dvLmpmzedvZ3qdZGuD0UlOmISs9gTn86e0+lEnckgK19bE+HjYkfvAA8i2rkT3s6DPfHpvLXqCA42lrw9LphRwa0azE4hxF4ppV7pSvUZ8UcAcVLKU2WN/wiMBSqLtwSchfaXcQLSgRKgjx51FQamRFfCkYtH2H1eG9FHp0STX5KPQNDVoysTu04kolUEvbx74WjteEXdx0Mfp1+rfszYMoMH1j7AY6GP8UjwI/ptjtEImL1vNgDP9HzGoO36OPrwer/XGb9iPIsOLeJfvf5l0PYNTviDcPxPWPcKBNwAXt0apt9Tm7QUEgOegdY9kVKSlJNEpG8kw7v7MCzQmyPJl1h/5AIbjlzgvT+P8d6fx2jjYc/Qrt4MC/QmIsADa0vj3aXkFpaw72xG2Yg+nf3nMiko1uZw2rd0ZFSQD73beRAR4IGfu/0VF6RurVzo38GT537ez+NL9nF7T19eH9sdFzvz2spRH+H3BSo7LxPQBL0yc4EVQBLgDNwtpdQJIfSpqzAQq0+tZs3pNURdiCKnOAeAjm4dua3jbfTx6UO4T/h1wxbL6eHVg6VjlvLWrreYt38eO5J2MGvgLFo5NdzoxRjEpsXyx6k/eDj4YaN8l07unRgZMJLvj37PpMBJtLA343QDQsDYufBpP/jlEXhkg7aXgTEpytX2QfZoD4NfACC9IJ2C0oKKPPxCCAJbuxDY2oWnh3biQnYBfx9NYf3hC/yw+yyLtsfjbGfFoM4tGRbozeDOXrg61E5UpZRk55eQnJ1PcmYByVkFnM/KJzmrgGMXLnEoKZtSncRCQPfWrkyMaEtEgDai93Sq+Rx19HLil8f788nfcczbGMeu0+m8f2co/TqYz+9BH+Gv6v7qav/QCGA/cCPQAfhLCLFFz7paJ0JMBaYC+Pv7V1VEUQ06qeOjvR+x6NAifJ18GRkwkgifCHr79MbT3rNObTrbODNr4CwGtB7AW7veYvyK8bza71VGBow0sPUNg5SS9/a8h4edBw8FPWS0fp4IfYI/4//kq9iv+G/v/xqtH4Pg5KWJ/w/3wN//B8PfNG5/G9+GjHh4YFXFvrdJOUnAVemYK+HtYseECH8mRPiTX1TK1rg01h++wIajKfxxMBlLC0Hvdu4VLqG2LRxIzy0qE/MCkrMvi/r58mNZBeQXl17RjxDg5WxLuxaOPDG4A73beRDW1h0n27ptWWJtacFzwzozpEtLnvv5ABO/3MnDkQH8e3gX7KxNf/esz7dKANpUeu+HNrKvzBRgltQmDOKEEKeBrnrWBUBKuQBYAJqPXy/rFRSVFvHytpdZc3oN93S5hxkRMwzqlhnTYQw9vHowY8sM/rP5P2xN3MoLfV64xkVk7vx99m/2pezjlb6vGHXSup1rO8a0H8NPR39icuBkvB29jdaXQegyCnpN0fau7TRMc/sYg4S92obw4Q9Cu8iKw0m5ZcJf3c5blbC3sWRYoObu0ekkBxIy2XAkhfVHLvB/q47wf6uOYG0pKC69Uj4sLQQ+Lnb4uNrRrbULN3b1wsfVjlau9mXPdrR0tjWK+6invzurno7k7dVH+GLLaTYfT+PDu0Pp3rrmO29jos/krhXaBO1QIBFtgnailPJQpTLzgQtSyteFEN7APiAUyKypblWoyV39uFR0iWc3Psuu87t4JuwZHgp6yGgTYCW6Ej478BlfxHyBr5Mv7wx8h+CWwUbpy9AUlxZz2++3YW1hzbJbl2FlYdyN5xIuJTBm+RjGdx7Py31fNmpfBqEoFz6/AYrztY1O7N0N235JESwYBAVZ8MRObTFZGQtjF/Lh3g/ZPmE7zjbOde7iXHoeG45cIDm7gFYudvi42tOqTNRbONliaWH6VBAbj6Xw32UHycwr4rlhXZh6Q3uD2lWbyV2klDU+gJvRBPwk8FLZsceAx8petwbWATFALHDf9erW9OjVq5esCzqdrk71GiMXci/I23+/Xfb4pof8Pe73Bus36nyUHLZ0mOzxTQ/5xcEvZElpSYP1XVe+PfStDFoUJDef29xgfb6x/Q3Z49seMuFSQoP1WS8S9kr5hoeUi8ZImXHGsG1vnCXlay5SHl1zzUdv7nhT9v++v2H7M2Mu5hTKxxZHybbT/5B3zN8mz6TlGqxtIErqoa9SyppH/KagLiN+KSUP/vkgIS1DmNB1Aj6OPkayrmpiUmNYHrecANcA7ul6D9YWxpvFP5V5isfWP0ZWYRYfDf6I/r79jdZXVWQXZTNzx0z+jP+TcO9w/jfwfw1+vvUlqzCLm3+9mSDPID676bMGCwk8n3ue0b+OZnT70cwcMLNB+qw3exfBmuna68hntcibMl98nUk5oq0ZCBwLd3x1zcdPrH+CtPw0fh7zc/36aURIKVkenchrvx9CJyWvjgnkrvA29f5t1mbE32RW7uYW5+Ju586iQ4sY+ctI/vvPf4lJjTFqnyW6EtbGr+W+1fcxcfVEfo/7nXf3vMsdK+5gR9IOo/QZnRLNpDWTKCotYuHIhQ0u+gAuNi68d8N7vDngTQ5dPMT4FePNdsXv5wc/J6c4h3+H/7tB48B9HH24q8tdrDi5gjPZZxqs33rR6wF4cg90Hgmb/gdze8Oh3+q+daOuVNtC0dYZRr1TZZGknCS9/PtNCSEEt4f5sfbZGwj2c2X6LzE88u1e0nIKG8yGJiP8TjZOfDj4Q1aNW8XEbhPZnLiZiasnMmn1JNbFr6NEV2KwvrIKs1gYu5BRv47iP//8h4v5F5kRMYN/7v6HuTfOpai0iKl/TeW5Tc9VRC0Ygg1nNvDIukdwt3Nn8c2LCWwRaLC2a4sQgts63sayMcvwd/bnuU3P8dr218grNp8kW2ezz/LD0R8Y13Ecnd07N3j/DwU/hLWFNfMPzG/wvuuMmz/c9Q1M/gPsXGHpZPhmDFy47rRc1ez6HBKjtEVijtdGl0kpScptfsJfjq+bPd8/3JeXR3dj84lURny0mb8OX2iQvpuMq+dqcopyWB63nCVHlpCYk4ivky8Tu07k9k631zmq43TWaZYcWcKKkyvIL8mnt09v7ut2H4P8Bl0RSVNYWsii2EV8GfMlAA8HP8wDQQ9ga1n3OOkfj/7I27veJtgzmLlD5+JuZ+AJuHpQrCvm0/2f8lXMV3g5ePF02NPc0v4WLIRpxxXPbnyWbUnbWH376jqHtdaXD/d+yKLYRSwfu5wObh1MYkOdKS2BfYu0UM+CLAh/CIa8CA4eNdfNiNfWCLQbCBN/qjLPfnpBOoN+GsT03tO5L/A+g5vfmDh2/hL/+mk/aTmF/POfwTjY1D4AweCTuw39qOvkblWUlJbIv+L/kvevvl8GLQqSfZb0kbN2zZLnss/pVV+n08ntidvlE+ufkEGLgmTPb3vKl7a8JI9cPFJj3aRLSfLZjc/KoEVBcuSykXLj2Y21noDW6XTy470fy6BFQfLJ9U/KvOK8WtVvSPZd2CfvXnm3DFoUJO9ccafcnbzbZLZEnY+SQYuC5Pz9801mg5RSpueny4jvIuSzG581qR31IveilH/8W8rX3aSc1VbK3V9Ieb1JfZ1Oym9ulfItXykzq/8/i02NlUGLguSGMxsMb3MjpKC4RB4/n13n+jTHyV19OJR2iG8Pf8u6+HXo0HFjmxuZFDiJnl49r/H/FpQUsOrUKr478h1xmXF42Hlwd5e7uavLXbUePe5M3sn/dv2PU1mnGOg7kOkR02nr0rbGesW6Yl7f/jorTq5gfCctNNDYoYj1RSd1rDm9htn7ZpOcm8xgv8E8G/4s7V3bN6gNE1dNJDU/lT/G/YG9VT0nKOvJ3Oi5fH7wc5aOWUpXj64mtaVenI/VcvnHb9ESrI16B9oNuLZc9Hfw+zQY/SH0rn6x3Lr4dfz7n3+zbMwyunh0MaLhzYPajPiblfCXcz73PD8e/ZGlx5eSXZRN9xbdmRQ4ieHthpNZkMmPx35k6bGlZBRm0Nm9M5MCJzEqYFS9XDXFumK+P/I98w/Mp6i0iMndJ/NI8CM4WDtUWT63OJd/b/o325K2Ma3HNB4NedRkSarqQkFJAd8d+Y4vY76koKSAOzvfyeM9HsfDTg83QT3549QfvLDlBd6KfItbO9xq9P5qIrsom5G/jKSXVy8+GfqJqc2pH1LC4d9h3cuQdU7b1nHYm+BWtk7z0nmYFwHeQdo8wXUyfy6KXcQHez9g24RtuNi4VFtOoR9K+PUkrziPlSdX8t2R74jPjqeFXQuyirIo1ZUyqM0g7g+8n3DvcIMKbmpeKh/v+5gVJ1fg7eDN872fZ0TbEVf0kZafxhPrn+B4xnFe7fcqt3e63WD9NzQX8y8y/8B8lh1fhr2VPQ8HP8x9gffV6yJ6PQpKChjz2xjcbd358ZYfTT7PUM6Cgwv4JPoTlty8hJCWIaY2p/4U5cH2ObD1I0CUhX8+rW3teHwdPL4dPDtet4m3dr7FqtOr2D5he8PY3MRRwl9LdFLH1sSt/HL8F1o5tWJi14n4uxg3X1B0SjRv73qbo+lHifCJ4IWIF+jo3pH4rHgeW/8Y6QXpvD/ofW7wM9IS+gbmVOYpPtr7EZsSNtHKsRXPhD3DqIBRBhfmL2O+ZPa+2Xw94mt6+/Q2aNv1Ibc4l1G/jKJbi258PuxzU5tjODLPahk+D/8Gji0hNxVuel27ENTAtA3TSMlLYemYpUY3szmghL+RUKorZdnxZcyJnkNucS63dbyNv8/+DcC8ofMaTUqE2rAreRcfRH3AkfQjBLUI4vnez9PLu5dB2k7LT2P0r6Pp06oPc26cY5A2DUm5a2PhiIWE++gXfNFoOL0F1r4ANo5aEjbLmueixv0+jjbObczyb9UYUcLfyMgoyOCT6E9YdnwZvk6+fD7sc6PfcZgSndSx8uRK5kTPISUvhaH+Q3m217N6TXiX6krJKMzgYv5F7VFwkbT8NC7mXyQ6NZrDaYdZPnY57VzbGf+L1JL8knxu/vVm2rq0ZeGIhY1qzkZvpKwydPPaYpI+3/dhfKfxTI+Y3gCGNX0MvRGLwsi427nzar9Xmdx9Mu527k1+ostCWDC241iGtxvOt4e+5avYr/jn3D/c3fVuBvoO5GLBxWuFvexYRkEGsorM3naWdrSwb8GzvZ41S9EHsLey55HgR/jf7v+xM3kn/Vr3M7VJhkfPi1lWYRb5JfnNdvGWqVEjfoXJSctPY97+efx64ld0UldxvFzMW9i10J4rvfa097zitYOVQ6MYQReVFjF6+Wi87L347ubvGoXNxuDQxUPc88c9fDzk4zpteK+4FjXiVzQqPO09ea3fazzY/UFS81MrBN7R2rHJCaONpQ2PhjzKGzveYEviliYzeV9bylOZ+Dr5mtiS5ol5xLopFEAblzaEeYfR1qUtTjam21zb2IztOBY/Jz/mRs+94g6nOVGx85Zy9ZgEJfwKRQNjbWHN4z0e50j6ETac3WBqc0xCYk4iztbOTX4+y1xRwq9QmIDRAaNp59KOT/d/SqmutOYKTYyknCSjbHiv0A8l/AqFCbC0sGRaj2nEZcaxNn6tqc1pcJpzOmZzQAm/QmEihrcbTif3Tsw/MN+g+0WYO1JKknKS1MSuCVHCr1CYCAthwbQe0ziTfYaVJ1ea2pwGI7som9ziXFo7qhG/qVDCr1CYkBvb3Ehgi0A+O/AZxaXFpjanQUjMSQRUKKcpUcKvUJgQIQRP9niSpNwklsctN7U5DYIK5TQ9SvgVChMT6RtJj5Y9+PzA5xSUFJjaHKNTPuJXwm86lPArFCZGCMFTPZ8iJT+FH4/+aGpzjE5SThKO1o4qht+EKOFXKMyAiFYRDGg9gA/3fsgn0Z806dj+8lDOproyuzGghF+hMBM+GvIRYzuOZcHBBUz9aypp+WmmNskoJOUk4euoJnZNiV7CL4QYKYQ4JoSIE0LMqOLz/wgh9pc9YoUQpUIIj7LP4oUQMWWfqZSbCkU12FvZ8+aAN3lzwJscTD3IHSvuYHfyblObZVDKY/iVf9+01Cj8QghLYB4wCggEJgghAiuXkVK+J6XsIaXsAbwA/COlTK9UZEjZ501s2yGFwvDc1vE2vh/9Pc42zjzy1yN8duCzJpPMLbsom5ziHCX8JkafEX8EECelPCWlLAJ+BMZep/wE4AdDGKdQNFc6uXfip1t+YmS7kczbP4/H1z9OekF6zRXNHJWO2TzQR/h9gXOV3ieUHbsGIYQDMBL4pdJhCawTQuwVQkytrhMhxFQhRJQQIio1NVUPsxSKpo2DtQOzBs7i1X6vEnU+ijtX3sm+C/tMbVa9KBd+laDNtOgj/FVNvVe3bdcYYNtVbp4BUsowNFfRNCFElTtPSCkXSCnDpZThLVu21MMshaLpI4Tgzs538t3N32FnaceDfz7I17FfN1rXT8WqXTW5a1L0Ef4EoE2l935AUjVl7+EqN4+UMqnsOQVYjuY6UigUtaBbi278eMuP3Oh/Ix/t/Yin/n6KzIJMU5tVa5Jzk3GwcsDV1tXUpjRr9BH+PUAnIUSAEMIGTdxXXF1ICOEKDAJ+r3TMUQjhXP4aGA7EGsJwhaK54WzjzAeDPmBGxAy2J23nrj/u4mDqQVObVSsScxJVDL8ZUKPwSylLgCeBP4EjwM9SykNCiMeEEI9VKjoOWCelzK10zBvYKoQ4AOwGVkkpm1/ycYXCQAghuLfbvSwetRiBYPLaySw+vBgpq/O+mhcqHbN5IMzxBxMeHi6jolTIv0JxPbIKs3h528tsOreJof5DmTlgptmnQej/fX9u6XALL/Z50dSmNDmEEHv1DZlXK3cVikaKq60rc4bM4fnw59l0bhN3r7ybo+lHTW1WtWQXZXOp+JIa8ZsBSvgVikaMEILJ3SezaOQiCksLeWnrS6Y2qVoqQjkdVSinqVHCr1A0AXp49eD+wPs5nnGc87nnTW1OlagNWMwHJfwKRRMh0jcSgK2JW01sSdUk5yQDKg+/OaCEX6FoInRw64CPo4/ZCn9iTiL2Vva42bqZ2pRmjxJ+haKJIIQg0jeSnck7zXL/3vJQThXDb3qU8CsUTYhI30hyi3PZn7rf1KZcQ/kGLArTo4RfoWhC9G3VFysLK7YkbjG1KdeQmJNIa0cl/OaAEn6FognhaO1ImFeY2fn5s4uyuVR0SY34zQQl/ApFEyPSN5ITGSfMKqxTRfSYF0r4FYomRnlY57bEbSa25DJqAxbzQgm/QtHE6OjWEW8Hb7Ny9yTlasKvRvzmgRJ+haKJcUVYp848wjrLY/jdbd1NbYoCJfwKRZNkoO9Acopz2J+y39SmAJqrp7WjysNvLijhVyiaIH1a9cFKWJmNnz8pJ0nts2tGKOFXKJogTjZO9PTuaTZ+/sScRDWxa0Yo4VcomiiRvpEcyzhGSl6KSe3IKcohuyhbTeyaEUr4FYomirmEdaqIHvNDCb9C0UTp5NYJLwcvk6dvqIjhd1SuHnNBCb9C0USpCOtM2kmJrsRkdpRvwKJG/OaDEn6FogkT6RvJpeJLHEg9YDIbknKSsLO0w8POw2Q2KK5ECb9C0YTp26ovVsLKpNE95aGcKobffFDCr1A0YZxtnAn1CjWp8CfmJCo3j5mhhF+haOJE+kZyNP0oqXmpJuk/OTdZTeyaGVb6FBJCjARmA5bAl1LKWVd9/h/g3kptdgNaSinTa6qrL8XFxSQkJFBQUFCX6oomgp2dHX5+flhbW5valEbDQN+BzN43m62JWxnXaVyD9p1bnEtmYaYa8ZsZNQq/EMISmAcMAxKAPUKIFVLKw+VlpJTvAe+VlR8DPFsm+jXW1ZeEhAScnZ1p166d8hU2U6SUXLx4kYSEBAICAkxtTqOhs3tnvOy9TCL8Kh2zeaKPqycCiJNSnpJSFgE/AmOvU34C8EMd61ZLQUEBLVq0UKLfjBFC0KJFC3XXV0uEEAzwHcCO5B0NHtZZLvxqxG9e6CP8vsC5Su8Tyo5dgxDCARgJ/FKHulOFEFFCiKjU1Kp9kUr0Feo3UDcifSO5VHSJg6kHG7RfFcNvnugj/FX9p8lqyo4Btkkp02tbV0q5QEoZLqUMb9mypR5mKRQKfenbui+WwrLBo3uScpKwtbSlhV2LBu1XcX30Ef4EoE2l935AUjVl7+Gym6e2dRUKhZFwsXEhtGXDh3Um5SbRylHF8Jsb+gj/HqCTECJACGGDJu4rri4khHAFBgG/17ZuU2fTpk3ccsstpjbD4Lz++uu8//77ADzwwAMsW7bMxBYprsdAv4EcST9CWn5ag/WZlJOkJnbNkBqFX0pZAjwJ/AkcAX6WUh4SQjwmhHisUtFxwDopZW5NdQ35BZojpaWlJqmraNyUZ+tsyFF/Uk6S8u+bIXrF8UspVwOrrzr22VXvFwGL9KlbX95YeYjDSdmGbJLA1i68NqZ7tZ/Hx8czcuRIIiMj2blzJ6GhoUyZMoXXXnuNlJQUlixZAsC//vUv8vPzsbe3Z+HChXTp0uWKdoKDg9myZQuurq54enry0Ucfcf/99zNp0iQmT55Mx44dmTRpErm52vVz7ty59O/fn02bNvHGG2/QqlUr9u/fT0xMDDNmzGDTpk0UFhYybdo0Hn300Sptr03dd999l8WLF2NhYcGoUaOYNWsWX3zxBQsWLKCoqIiOHTuyePFiHBwcDHHaFQ1IF/cueNp7sjVxK7d1vM3o/eUV55FRmKGE3wzRS/gVGnFxcSxdupQFCxbQu3dvvv/+e7Zu3cqKFSt4++23+fbbb9m8eTNWVlasX7+eF198kV9++eWKNgYMGMC2bdto27Yt7du3Z8uWLdx///3s3LmT+fPnY2FhwV9//YWdnR0nTpxgwoQJREVFAbB7925iY2MJCAhgwYIFuLq6smfPHgoLCxkwYADDhw+vNr5dn7pHjx7lt99+Y9euXTg4OJCers3R33777TzyyCMAvPzyy3z11Vc89dRTRjzTCmMghGBA6wH8fe5vSnQlWFkY999fxfCbL41S+K83MjcmAQEBBAcHA9C9e3eGDh2KEILg4GDi4+PJyspi8uTJnDhxAiEExcXF17QxcOBANm/eTNu2bXn88cdZsGABiYmJeHh44OTkRFZWFk8++ST79+/H0tKS48ePV9SNiIioEPZ169Zx8ODBCr96VlYWJ06cqFb49am7fv16pkyZUjGa9/DQsinGxsby8ssvk5mZSU5ODiNGjDDE6VSYgEi/SH4/+TsxaTH09Opp1L7KN2Bp5aj22jU3GqXwmwpbW9uK1xYWFhXvLSwsKCkp4ZVXXmHIkCEsX76c+Ph4Bg8efE0bN9xwA/PmzePs2bO89dZbLF++nGXLljFw4EAAPvroI7y9vTlw4AA6nQ47O7uKuo6OjhWvpZR88skneouwPnXXrl1bZfTFAw88wG+//UZoaCiLFi1i06ZNevWpMD/6teqHhbBgS8IWowt/eQy/GvGbHypJmwHJysrC11f7kS9atKjKMm3atCEtLY0TJ07Qvn17IiMjef/99yuEPysri1atWmFhYcHixYurnYwdMWIE8+fPr7irOH78eMW8QE1UV3f48OF8/fXX5OXlAVS4ei5dukSrVq0oLi6umMtQNE5cbV0JbRnKtiTjb8eYlJOEjYUNLexVDL+5oYTfgPz3v//lhRdeYMCAAdeNnunTpw+dO3cGNNdPYmIikZFaxMUTTzzBN998Q9++fTl+/PgVI/XKPPzwwwQGBhIWFkZQUBCPPvooJSX6Lcevru7IkSO59dZbCQ8Pp0ePHhWhmm+++SZ9+vRh2LBhdO3atTanRGGGRPpGcvjiYaOHdZZH9FgIJTPmhpCyukW4piM8PFyWT2iWc+TIEbp162YiixTmhPot1I/DFw9z9x9381bkW9za4Vaj9TPhjwm42Lrw+bDPjdaH4jJCiL1SynB9yqpLsULRzOjq0ZUWdi3YmmDceP6kXBXDb66oyd0mRExMDJMmTbrimK2tLbt27TKRRQpzxEJYMMB3AP8k/EOprhRLC0uD95FXnEd6Qbqa2DVTlPA3IYKDg9m/f7+pzVA0Agb6DmTFyRXEpMXQw6uHwdtPzk0GVCinuaJcPQpFM6Rfay2s01jpG1Qop3mjhF+haIa42roS4hliNOFXG7CYN0r4FYpmSqRvJIcuHuJi/kWDt52Um4S1hTWe9p4Gb1tRf5TwKxTNlEg/be3I9qTtBm9bxfCbN+qvoifx8fEEBQXpXd5U+elra6ei+dLNoxsedh5sSdxi8LaTcpJo7ajcPOZK44zqWTMDzscYtk2fYBg1y7BtNnNKS0uxtDR8qKDCMFgICyJ9I40S1pmYk8iQNkMM1p7CsKgRfy0oKSlh8uTJhISEcMcdd5CXl8fMmTPp3bs3QUFBTJ06lapWQldXZvDgwUyfPp2IiAg6d+7Mli3ayKu0tJTnn3+e4OBgQkJC+OSTTwDYu3cvgwYNolevXowYMYLk5OSK46GhofTr14958+Zd9zvEx8czcOBAwsLCCAsLY/v2y7f57777LsHBwYSGhjJjxgxAS0V90003ERoaSlhYGCdPnrxmR7Enn3yyIjdRu3btmDlzJpGRkSxdupQvvviC3r17Exoayvjx4yvyAF24cIFx48YRGhpKaGgo27dv55VXXmH27NkV7b700kvMmTOnVn8jRe2I9I0kqzCL2IuxBmszvySf9IJ0NbFrzkgpze7Rq1cveTWHDx++5lhDcvr0aQnIrVu3SimlnDJlinzvvffkxYsXK8rcd999csWKFVJKKSdPniyXLl0qpZTVlhk0aJB87rnnpJRSrlq1Sg4dOlRKKeWnn34qb7/9dllcXFxRv6ioSPbr10+mpKRIKaX88ccf5ZQpU6SUUgYHB8tNmzZJKaV8/vnnZffu3av9Hrm5uTI/P19KKeXx48dl+blevXq17Nevn8zNzb3C5oiICPnrr79KKaXMz8+Xubm5cuPGjXL06NEVbU6bNk0uXLhQSill27Zt5TvvvFPxWVpaWsXrl156Sc6ZM0dKKeVdd90lP/roIymllCUlJTIzM1OePn1a9uzZU0opZWlpqWzfvv0V9csx9W+hKZGRnyFDvgmRc6PnGqzNkxknZdCiILny5EqDtamoGSBK6qmxjdPVYyLatGnDgAEDALjvvvuYM2cOAQEBvPvuu+Tl5ZGenk737t0ZM2bMFfU2btxYbZnbb78dgF69ehEfHw/A+vXreeyxx7Cy0v48Hh4exMbGEhsby7BhwwDtrqBVq1ZkZWWRmZnJoEGDAJg0aRJr1qyp9jsUFxdXme+/qlz8ly5dIjExkXHjxgFckSL6etx9990Vr6vL5f/333/z7bffAmBpaYmrqyuurq60aNGC6OhoLly4QM+ePWnRQmV2NCZudm4EeQaxNWEr03pMM0ib5Xn4VQy/+aKEvxZcnateCMETTzxBVFQUbdq04fXXX6egoOCKMgUFBdctU57T39LSsiK7ppTymr6klHTv3p0dO3ZccTwzM7PKHPrVUV2+/+r6rAorKyt0Ot0V37EylTOK1jaX/8MPP8yiRYs4f/48Dz74oN7fS1F3In0jmb9/PukF6XjYedS7vYoYfjW5a7YoH38tOHv2bIXw/vDDDxWplD09PcnJyakyiqdcFK9X5mqGDx/OZ599VnEhSE9Pp0uXLqSmplb0X1xczKFDh3Bzc8PV1ZWtW7WFODXly68u339VufhdXFzw8/Pjt99+A6CwsJC8vDzatm3L4cOHKSwsJCsriw0bNlTbX3W5/IcOHcr8+fMB7e4lO1vbQ3ncuHGsXbuWPXv2qJ2+GoiBvgORSLYlGiZHf2JOIlYWVrR0aGmQ9hSGRwl/LejWrRvffPMNISEhpKen8/jjj/PII48QHBzMbbfdRu/eva+p4+bmVmOZq3n44Yfx9/cnJCSE0NBQvv/+e2xsbFi2bBnTp08nNDSUHj16VEzMLly4kGnTptGvXz/s7e2v23Z1+f6ry8W/ePFi5syZQ0hICP379+f8+fO0adOGu+66i5CQEO6991569qx+J6fqcvnPnj2bjRs3EhwcTK9evTh06BAANjY2DBkyhLvuuktFBDUQgS0C8bDzMNgq3vJQThXDb76ofPwKs0Kn0xEWFsbSpUvp1KlTlWXUb8HwvLDlBbYmbmXTXZvqHdZ576p7sbe258vhXxrIOoU+qHz8ikbJ4cOH6dixI0OHDq1W9BXGIdI3kszCTA5dPFTvthJzEtXErpmjJnebKH/++SfTp0+/4lhAQADLly83kUU1ExgYyKlTp0xtRrOkf+v+CARbE7cS0jKkzu0UlBRwseCimtg1c/QSfiHESGA2YAl8KaW8ZomrEGIw8DFgDaRJKQeVHY8HLgGlQIm+tyKK+jFixAg1OarQG3c7d4JbBrP0+FIG+A4gtGVondopz8OvFm+ZNzW6eoQQlsA8YBQQCEwQQgReVcYN+BS4VUrZHbjzqmaGSCl7KNFXKMyXV/u+iq2lLQ+sfYAlR5ZUG857PcpDOZWrx7zRx8cfAcRJKU9JKYuAH4GxV5WZCPwqpTwLIKVMMayZCoXC2HTx6MJPt/zEgNYDmLV7Fv/d/F9yi3Nr1Ub5BixqxG/e6CP8vsC5Su8Tyo5VpjPgLoTYJITYK4S4v9JnElhXdnxqdZ0IIaYKIaKEEFGpqan62q9QKAyIq60rc26cw7/C/sW6M+u45497iMuI07t+Uk6SFsNvr2L4zRl9hL+qZaFX3wNaAb2A0cAI4BUhROeyzwZIKcPQXEXThBA3VNWJlHKBlDJcShnesqX60SgUpsJCWPBQ8EN8OfxLLhVdYuLqiaw8uVKvukk5Sfg4+BhlA3eF4dBH+BOANpXe+wFJVZRZK6XMlVKmAZuBUAApZVLZcwqwHM111OgwVJ77qKgonn76aQNYZHycnJwAleO/udLbpzdLxywlsEUgL259kZk7ZlJYWnjdOom5KpSzMaBPVM8eoJMQIgBIBO5B8+lX5ndgrhDCCrAB+gAfCSEcAQsp5aWy18OBmfU1+p3d73A0/Wh9m7mCrh5dmR4xveaC9SQ8PJzwcOPNcZeUlFQkd1Mo6ktLh5Z8OfxLPon+hK9jv+bQxUN8MOgD/Jz9qiyflJPEQN+BDWylorbUOOKXUpYATwJ/AkeAn6WUh4QQjwkhHisrcwRYCxwEdqOFfMYC3sBWIcSBsuOrpJRrjfNVjE9V+firy5FfXa79yrnsU1NTGTZsGGFhYTz66KO0bduWtLQ04uPj6datG4888gjdu3dn+PDh5OfnV2vX4MGDefHFFxk0aBCzZ8+u1qaqcuvn5OQwdOhQwsLCCA4O5vfffzfyWVQ0NqwsrHi217PMGTKHc9nnuOuPu9h0btM15QpLC0nLT1MTu40BffM3N+SjseTjf/fdd6vNkV9drv3KueynTZsm3377bSmllGvWrJGATE1NladPn5aWlpYyOjpaSinlnXfeKRcvXlytbYMGDZKPP/64lFJeN29/Vbn1i4uLZVZWlpRSytTUVNmhQwep0+mklFI6OjpWfPfr5fhvaEz9W2jOnM0+K+9ccacMWhQkP4r6SBaXFld8djrztAxaFCRXxK0woYXNF1Q+fuNwdT7+t99+u8oc+eVUlWu/Mlu3bq1YSTty5Ejc3d0rPgsICKBHjx7XrV+Z8hz4x44dq9Km6nLrFxcX8+KLL7J582YsLCxITEzkwoUL+Pj41ObUKJoJbZzbsPjmxczaPYuvYr/iYNpB3r3hXTztPS+nY1YjfrNHCX8tuDpfvbOzc5U58supKtd+ZeR1FsiU1y2vfz1XD1zOgS+rydtfnvb4apYsWUJqaip79+7F2tqadu3aXZNfX6GojK2lLa/1e42eXj15c8eb3LnyTt674T0Sc7UYfjW5a/6oJG214Op8/H379q0yR76+REZG8vPPPwOwbt06MjIy6m1jdXn7q8utn5WVhZeXF9bW1mzcuJEzZ87U2wZF8+DWDreyZPQSnKydeHjdw3x/5HushIrhbwwo4a8FV+fjf+qpp6rNka8Pr732GuvWrSMsLIw1a9bQqlUrnJ2d62Xj9fL2V5Vb/9577yUqKorw8HCWLFlyRc58haImOrt35ofRPzDUfyhxmXF4O3qrGP5GgMrHb0IKCwuxtLTEysqKHTt28Pjjj7N//35Tm2X2NMXfQmNHSslvcb9ha2nLze1vNrU5zZLa5ONXPn4TcvbsWe666y50Oh02NjZ88cUXpjZJoagTQgjGdRpnajMUeqKE34R06tSJ6OhovctPmzaNbduu3Bf1mWeeYcqUKYY2TaFQNGEalfBLKa+JrGlOzJs3z9QmmBxzdE0qFI2NRjO5a2dnx8WLF9U/fjNGSsnFixcr1iAoFIq60WhG/H5+fiQkJKBSNjdv7Ozs8POrOk+MQqHQj0Yj/NbW1gQEBJjaDIVCoWj0NBpXj0KhUCgMgxJ+hUKhaGYo4VcoFIpmhlmu3BVCpALVJY3xBNIa0JzaouyrH8q++qHsqx+N2b62Ukq9EiWZpfBfDyFElL7Lkk2Bsq9+KPvqh7KvfjQX+5SrR6FQKJoZSvgVCoWimdEYhX+BqQ2oAWVf/VD21Q9lX/1oFvY1Oh+/QqFQKOpHYxzxKxQKhaIeKOFXKBSKZobZCL8QYqQQ4pgQIk4IMaOKz4UQYk7Z5weFEGH61jUD++KFEDFCiP1CiKir6zaQfV2FEDuEEIVCiOdrU9cM7DOH83dv2d/1oBBiuxAiVN+6ZmCf0c+fnjaOLbNvvxAiSggRqW9dM7DP5L/BSuV6CyFKhRB31LZuBVJKkz8AS+Ak0B6wAQ4AgVeVuRlYAwigL7BL37qmtK/ss3jA08TnzwvoDbwFPF+buqa0z4zOX3/Avez1KDP8/VVpX0Ocv1rY6MTlecUQ4KiZncMq7TOX32Clcn8Dq4E76nr+zGXEHwHESSlPSSmLgB+BsVeVGQt8KzV2Am5CiFZ61jWlfQ1BjfZJKVOklHuA4trWNbF9DYE+9m2XUmaUvd0J+Olb18T2NRT62Jgjy5QKcASkvnVNbF9DoO85eAr4BUipQ90KzEX4fYFzld4nlB3Tp4w+dU1pH2g/oHVCiL1CiKkGtk1f+4xRV1/q24e5nb+H0O7u6lK3LtTHPjD++QM9bRRCjBNCHAVWAQ/Wpq4J7QMz+A0KIXyBccBnta17NeaSj7+q/RSvvtpWV0afuvWlPvYBDJBSJgkhvIC/hBBHpZSbG9g+Y9TVl/r2YTbnTwgxBE1Yy/2/ZnX+qrAPjH/+9LZRSrkcWC6EuAF4E7hJ37r1pD72gXn8Bj8GpkspS8WVW9DW+vyZy4g/AWhT6b0fkKRnGX3qmtI+pJTlzynAcrRbs4a2zxh19aVefZjL+RNChABfAmOllBdrU9eE9jXE+dPbxko2bQY6CCE8a1vXBPaZy28wHPhRCBEP3AF8KoS4Tc+6V2KsyYpaTmxYAaeAAC5PTnS/qsxorpw83a1vXRPb5wg4V3q9HRjZ0PZVKvs6V07umsX5u459ZnH+AH8gDuhf1+9mIvuMfv5qYWNHLk+ehgGJZf8v5nIOq7PPLH6DV5VfxOXJ3VqfP4P+8ev5xW8GjqPNTr9Uduwx4LGy1wKYV/Z5DBB+vbrmYh/aTPuBsschE9rngzYyyAYyy167mNH5q9I+Mzp/XwIZwP6yR5SZ/f6qtK+hzp+eNk4vs2E/sAOINLNzWKV95vIbvKrsIsqEvy7nT6VsUCgUimaGufj4FQqFQtFAKOFXKBSKZoYSfoVCoWhmKOFXKBSKZoYSfoVCoWhmKOFXKBSKZoYSfoVCoWhm/D/2skqu1SzNLAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", @@ -4268,38 +3108,13 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 39, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", "plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", - "\n", "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", - "\n", "# plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", "plt.legend()" diff --git a/Model Training/.ipynb_checkpoints/XGBoost_with_hash-checkpoint.ipynb b/Model Training/.ipynb_checkpoints/XGBoost_with_hash-checkpoint.ipynb index c7dec80..ddcdeb9 100644 --- a/Model Training/.ipynb_checkpoints/XGBoost_with_hash-checkpoint.ipynb +++ b/Model Training/.ipynb_checkpoints/XGBoost_with_hash-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -79,29 +79,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", + "# covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", + "\n", + "X = pd.read_csv('../data/Training_Data/Training_Dataset_with_threshold.csv')\n", "repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", - "covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')" + "covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "y = pd.read_csv('../data/Training_Data/Labels.csv')\n", - "repackaged_benign_test_y = pd.read_csv('../data/Test_Data/Labels_Repackaged_Benign_Test.csv')\n", - "COVID_test_y = pd.read_csv('../data/Test_Data/Labels_COVID_Test.csv')" + "y = pd.read_csv('../data/Training_Data/Labels_trainingset.csv')\n", + "repackaged_benign_test_y = pd.read_csv('../data/Test_Data/Labels_testset.csv')\n", + "COVID_test_y = pd.read_csv('../data/Test_Data/Labels_COVID_testset.csv')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -132,16 +136,27 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Series([], Name: count, dtype: float64)" + "Permission: FACTORY_TEST 9538.0\n", + "Permission: DUMP 9538.0\n", + "Permission: BATTERY_STATS 9538.0\n", + "Permission: BIND_WALLPAPER 9538.0\n", + "Permission: BIND_INPUT_METHOD 9538.0\n", + " ... \n", + "step_detector 9538.0\n", + "temperature 9538.0\n", + "if_the_app_using_suspicious_libs 9538.0\n", + "malwareNum 9538.0\n", + "proportion 9538.0\n", + "Name: count, Length: 866, dtype: float64" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -159,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -169,6 +184,26 @@ "covid_test_X.drop(drop_features, axis = 1, inplace = True)" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "531" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(drop_features)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -184,7 +219,7 @@ "source": [ "X['label']=y['label']\n", "# Create dataset without sensor features in order to see if there's some improvement by adding sensor features\n", - "sensor_lst=list(X.iloc[:,-32:-3].columns)\n", + "sensor_lst=list(X.iloc[:,-14:-3].columns)\n", "# sensor_lst.remove('if_the_app_using_suspicious_libs')\n", "X_wo_sensors = X.drop(sensor_lst, axis = 1)\n", "\n", @@ -196,6 +231,36 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['accelerometer',\n", + " 'gravity',\n", + " 'gyroscope',\n", + " 'light',\n", + " 'linear_acceleration',\n", + " 'magnetic_field',\n", + " 'orientation',\n", + " 'proximity',\n", + " 'rotation_vector',\n", + " 'temperature',\n", + " 'if_the_app_using_suspicious_libs']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sensor_lst" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "# Train test split\n", @@ -219,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -275,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -284,25 +349,26 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The mean test score of XGBoost model with sensor features is: 0.8729662145476617\n" + "The mean test score of XGBoost model with sensor features is: 0.8787996882307093\n" ] } ], "source": [ - "avg_val_score = np.mean(cross_val_score(estimator, X_train_resample.drop(['proportion','label'],axis=1), np.squeeze(y_train_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", + "# avg_val_score = np.mean(cross_val_score(estimator, X_train_resample.drop(['proportion','label'],axis=1), np.squeeze(y_train_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", + "avg_val_score = np.mean(cross_val_score(estimator, X_train_resample.drop(['malwareNum','label'],axis=1), np.squeeze(y_train_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", "print('The mean test score of XGBoost model with sensor features is:', avg_val_score)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -314,7 +380,9 @@ } ], "source": [ - "avg_val_score = np.mean(cross_val_score(estimator, X_train_wo_sensor_resample.drop(['proportion','label'],axis=1), np.squeeze(y_train_wo_sensor_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", + "# avg_val_score = np.mean(cross_val_score(estimator, X_train_wo_sensor_resample.drop(['proportion','label'],axis=1), np.squeeze(y_train_wo_sensor_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", + "\n", + "avg_val_score = np.mean(cross_val_score(estimator, X_train_wo_sensor_resample.drop(['malwareNum','label'],axis=1), np.squeeze(y_train_wo_sensor_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", "print('The mean test score of XGBoost model without sensor features is:', avg_val_score)" ] }, @@ -392,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -413,10 +481,16 @@ " objective='binary:logistic',\n", " seed = seed)\n", " # Fit the estimator\n", - " estimator_function.fit(X_train_resample.drop(['proportion','label'],axis=1),X_train_resample['label'])\n", + "# estimator_function.fit(X_train_resample.drop(['proportion','label'],axis=1),X_train_resample['label'])\n", + " estimator_function.fit(X_train_resample.drop(['malwareNum','proportion','label'],axis=1),X_train_resample['label'])\n", + " \n", + " \n", " \n", " # calculate out-of-the-box roc_score using validation set 1\n", - " probs = estimator_function.predict_proba(X_eval.drop(['proportion','label'],axis=1))\n", + "# probs = estimator_function.predict_proba(X_eval.drop(['proportion','label'],axis=1))\n", + " probs = estimator_function.predict_proba(X_eval.drop(['malwareNum','proportion','label'],axis=1))\n", + " \n", + " \n", " probs = probs[:,1]\n", " val1_roc = roc_auc_score(y_eval,probs)\n", " \n", @@ -426,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -448,10 +522,10 @@ " objective='binary:logistic',\n", " seed = seed)\n", " # Fit the estimator\n", - " estimator_function.fit(X_train_wo_sensor_resample.drop(['proportion','label'],axis=1),X_train_wo_sensor_resample['label'])\n", + " estimator_function.fit(X_train_wo_sensor_resample.drop(['proportion','malwareNum','label'],axis=1),X_train_wo_sensor_resample['label'])\n", " \n", " # calculate out-of-the-box roc_score using validation set 1\n", - " probs = estimator_function.predict_proba(X_eval_wo_sensor.drop(['proportion','label'],axis=1))\n", + " probs = estimator_function.predict_proba(X_eval_wo_sensor.drop(['proportion','malwareNum','label'],axis=1))\n", " probs = probs[:,1]\n", " val1_roc = roc_auc_score(y_eval_wo_sensor,probs)\n", " \n", @@ -461,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -470,29 +544,29 @@ "text": [ "| iter | target | learni... | max_depth | n_esti... | reg_alpha |\n", "-------------------------------------------------------------------------\n", - "| \u001b[0m 1 \u001b[0m | \u001b[0m 0.9466 \u001b[0m | \u001b[0m 0.3751 \u001b[0m | \u001b[0m 17.81 \u001b[0m | \u001b[0m 95.5 \u001b[0m | \u001b[0m 0.07568 \u001b[0m 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"=========================================================================\n" ] } @@ -537,14 +611,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'target': 0.9582351434426231, 'params': {'learning_rate': 0.28729719343540316, 'max_depth': 22.71785408959317, 'n_estimators': 14.904823930846312, 'reg_alpha': 0.9535908701007884}}\n" + "{'target': 0.974735696517413, 'params': {'learning_rate': 0.11303958411260817, 'max_depth': 17.968946448214748, 'n_estimators': 65.73295322863495, 'reg_alpha': 0.3079653289036747}}\n" ] } ], @@ -554,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -563,29 +637,29 @@ "text": [ "| iter | target | learni... | max_depth | n_esti... | reg_alpha |\n", "-------------------------------------------------------------------------\n", - "| \u001b[0m 1 \u001b[0m | \u001b[0m 0.9486 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| \u001b[0m 0.4538 \u001b[0m | \u001b[0m 21.72 \u001b[0m | \u001b[0m 15.81 \u001b[0m | \u001b[0m 0.6936 \u001b[0m |\n", "=========================================================================\n" ] } @@ -625,19 +699,19 @@ "# kappa is a measure of 'aggressiveness' of the bayesian optimization process\n", "# The algorithm will randomly choose 3 points to establish a 'prior', then will perform \n", "# 10 interations to maximize the value of estimator function\n", - "xgbcBO_wo.maximize(init_points=3,n_iter=20,acq='ucb', kappa= 3, **gp_params)\n" + "xgbcBO_wo.maximize(init_points=3,n_iter=20,acq='ucb', kappa= 3, **gp_params)" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'target': 0.9573044740437158, 'params': {'learning_rate': 0.4654869515316642, 'max_depth': 21.127970235007517, 'n_estimators': 11.435220242864505, 'reg_alpha': 0.9373015690531954}}\n" + "{'target': 0.9696050995024875, 'params': {'learning_rate': 0.3402860424080705, 'max_depth': 21.608011621945234, 'n_estimators': 14.66211514786415, 'reg_alpha': 0.8880013815034622}}\n" ] } ], @@ -654,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -669,28 +743,30 @@ " print(param_dct)\n", " continuous = make_column_selector(dtype_exclude = 'object')\n", " model = xgb.XGBClassifier(**param_dct)\n", - " model.fit(X_train.drop(['proportion','label'],axis=1), np.squeeze(y_train.to_numpy().reshape(1, -1)))\n", + "# model.fit(X_train.drop(['proportion','label'],axis=1), np.squeeze(y_train.to_numpy().reshape(1, -1)))\n", + " model.fit(X_train, np.squeeze(y_train.to_numpy().reshape(1, -1)))\n", " y_pred = model.predict_proba(X_test)\n", " return model,y_pred" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "{'learning_rate': 0.4654869515316642, 'max_depth': 21, 'n_estimators': 11, 'reg_alpha': 0.9373015690531954}\n" + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "{'learning_rate': 0.3402860424080705, 'max_depth': 21, 'n_estimators': 14, 'reg_alpha': 0.8880013815034622}\n" ] } ], "source": [ - "pipe_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample,y_train_resample,X_test.drop(['proportion','label'],axis=1),y_test)\n", - "pipe_without_sensor,y_pred_wo_sensor = model_create_fit(xgbcBO_wo,X_train_wo_sensor_resample,y_train_wo_sensor_resample,X_test_wo_sensor.drop(['proportion','label'],axis=1),y_test_wo_sensor)" + "pipe_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample.drop(['malwareNum','proportion','label'],axis=1),y_train_resample,X_test.drop(['malwareNum','proportion','label'],axis=1),y_test)\n", + "pipe_without_sensor,y_pred_wo_sensor = model_create_fit(xgbcBO_wo,X_train_wo_sensor_resample.drop(['malwareNum','proportion','label'],axis=1),y_train_wo_sensor_resample,X_test_wo_sensor.drop(['malwareNum','proportion','label'],axis=1),y_test_wo_sensor)\n", + "\n" ] }, { @@ -746,7 +822,58 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AUC (including sensor features) 0.9630916074114648\n", + "AUC (not including sensor features) 0.9517960242062395\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10, 6))\n", + "fpr, tpr, _ = roc_curve(y_test, y_pred_with_sensor[:, 1])\n", + "fpr2, tpr2, _2 = roc_curve(y_test_wo_sensor, y_pred_wo_sensor[:, 1])\n", + "plt.plot(fpr, tpr, lw=2, label='With sensor features')\n", + "plt.plot(fpr2, tpr2, lw=2, label='Without sensor features')\n", + "print('AUC (including sensor features)', roc_auc_score(y_test, y_pred_with_sensor[:, 1]))\n", + "print('AUC (not including sensor features)', roc_auc_score(y_test, y_pred_wo_sensor[:, 1]))\n", + "\n", + "plt.xlabel('False Positive Rate', fontsize = 13)\n", + "plt.ylabel('True Positive Rate', fontsize = 13)\n", + "plt.title('ROC Curve', fontsize = 15)\n", + "plt.legend(fontsize = 13)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -758,19 +885,19 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([0.63293651, 0.95249131]),\n", - " array([0.79551122, 0.89885183]),\n", - " array([0.70497238, 0.92489451]),\n", - " array([ 401, 1829]))" + "(array([0.74318182, 0.94959128]),\n", + " array([0.81546135, 0.92501659]),\n", + " array([0.77764566, 0.93714286]),\n", + " array([ 401, 1507]))" ] }, - "execution_count": 98, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -781,19 +908,19 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([0.62173038, 0.94691287]),\n", - " array([0.77057357, 0.89721159]),\n", - " array([0.68819599, 0.92139248]),\n", - " array([ 401, 1829]))" + "(array([0.72321429, 0.94726027]),\n", + " array([0.80798005, 0.91771732]),\n", + " array([0.76325088, 0.9322548 ]),\n", + " array([ 401, 1507]))" ] }, - "execution_count": 99, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -818,15 +945,15 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Balanced Average Accuracy (including sensor features): 0.8471815267735527\n", - "Balanced Average Accuracy (not including sensor features): 0.8338925785590698\n" + "Balanced Average Accuracy (including sensor features): 0.8702389679417912\n", + "Balanced Average Accuracy (not including sensor features): 0.8628486845262424\n" ] } ], @@ -844,7 +971,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -853,13 +980,17 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# Train test split\n", - "X_ = pd.read_csv('../data/Training_Data/Training_Dataset.csv')\n", - "y_ = pd.read_csv('../data/Training_Data/Labels.csv')\n", + "X_ = pd.read_csv('../data/Training_Data/Training_Dataset_with_threshold.csv')\n", + "y_ = pd.read_csv('../data/Training_Data/Labels_trainingset.csv')\n", + "\n", + "# X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", + "# covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", "\n", "X_train_, X_test_, y_train_, y_test_ = train_test_split(X_,y_['label'], \n", " test_size = 0.2, \n", @@ -869,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -906,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -986,45 +1117,22 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for paired apps (including sensor features): 0.8502202643171806\n", - "Accuracy for non-paired apps (including sensor features): 0.8662006989515726\n", - "Accuracy for paired apps (not including sensor features): 0.8325991189427313\n", - "Accuracy for non-paired apps (not including sensor features): 0.8627059410883674\n" - ] - } - ], - "source": [ - "# print(\"Accuracy for paired apps (including sensor features):\", np.sum(paired_w_sensor_accuracy)/len(paired_w_sensor_accuracy))\n", - "# print(\"Accuracy for non-paired apps (including sensor features):\", np.sum(non_paired_w_sensor_accuracy)/len(non_paired_w_sensor_accuracy))\n", - "\n", - "# print(\"Accuracy for paired apps (not including sensor features):\", np.sum(paired_wo_sensor_accuracy)/len(paired_wo_sensor_accuracy))\n", - "# print(\"Accuracy for non-paired apps (not including sensor features):\", np.sum(non_paired_wo_sensor_accuracy)/len(non_paired_wo_sensor_accuracy))" - ] - }, - { - "cell_type": "code", - "execution_count": 102, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy for paired apps (including sensor features): 0.8414096916299559\n", - "Accuracy for non-paired apps (including sensor features): 0.8846729905142287\n", - "Accuracy for paired apps (not including sensor features): 0.8370044052863436\n", - "Accuracy for non-paired apps (not including sensor features): 0.8786819770344483\n" + "Accuracy for paired apps (including sensor features): 0.8819875776397516\n", + "Accuracy for non-paired apps (including sensor features): 0.9038351459645106\n", + "Accuracy for paired apps (not including sensor features): 0.8571428571428571\n", + "Accuracy for non-paired apps (not including sensor features): 0.8981110475100171\n" ] } ], "source": [ + "## update\n", "print(\"Accuracy for paired apps (including sensor features):\", np.sum(paired_w_sensor_accuracy)/len(paired_w_sensor_accuracy))\n", "print(\"Accuracy for non-paired apps (including sensor features):\", np.sum(non_paired_w_sensor_accuracy)/len(non_paired_w_sensor_accuracy))\n", "\n", @@ -1069,16 +1177,16 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2858, 324)" + "(2858, 322)" ] }, - "execution_count": 105, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1089,7 +1197,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1099,7 +1207,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1108,14 +1216,14 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy (including sensor features): 0.8400979706088173\n" + "Accuracy (including sensor features): 0.8908327501749476\n" ] } ], @@ -1125,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1134,14 +1242,14 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy (without sensor features): 0.8512946116165151\n" + "Accuracy (without sensor features): 0.9748075577326802\n" ] } ], @@ -1201,7 +1309,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1212,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1246,72 +1354,72 @@ " \n", " 0\n", " 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81\n", - " 162\n", - " 118\n", - " 0.728395\n", + " 111\n", + " 85\n", + 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" \n", " 7\n", " 6C699C8D1F7157366994ACDA5495051F2C58D7AB\n", - " 44\n", - " 44\n", + " 38\n", + " 38\n", " 1.000000\n", " \n", " \n", " 8\n", - " 699512C8B49E7A01A622BD250544E09A80A42D55\n", - " 39\n", - " 34\n", - " 0.871795\n", + " 5BFC55F389F4B5427341E4320A501711140AE444\n", + " 36\n", + " 24\n", + " 0.666667\n", " \n", " \n", " 9\n", - " 5BFC55F389F4B5427341E4320A501711140AE444\n", - " 35\n", - " 19\n", - " 0.542857\n", + " 699512C8B49E7A01A622BD250544E09A80A42D55\n", + " 33\n", + " 26\n", + " 0.787879\n", " \n", " \n", "\n", @@ -1319,19 +1427,19 @@ ], "text/plain": [ " ThumbPrint N correct score\n", - "0 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81 162 118 0.728395\n", - "1 28EAC321D548B4247D9C84810C0656EC9426716B 97 82 0.845361\n", - "2 F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284 71 71 1.000000\n", - "3 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03 67 67 1.000000\n", - "4 00F7DCC41988D8642C51D4F8BA5A42C413275885 58 58 1.000000\n", - "5 F243B92AD5EABA98BD43084864C4D5483F191CD9 52 44 0.846154\n", - "6 45A195BE1E17B3AFA086623DCC4661DEE2043B70 45 45 1.000000\n", - "7 6C699C8D1F7157366994ACDA5495051F2C58D7AB 44 44 1.000000\n", - "8 699512C8B49E7A01A622BD250544E09A80A42D55 39 34 0.871795\n", - "9 5BFC55F389F4B5427341E4320A501711140AE444 35 19 0.542857" + "0 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81 111 85 0.765766\n", + "1 F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284 71 71 1.000000\n", + "2 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03 55 55 1.000000\n", + "3 28EAC321D548B4247D9C84810C0656EC9426716B 54 39 0.722222\n", + "4 45A195BE1E17B3AFA086623DCC4661DEE2043B70 51 51 1.000000\n", + "5 F243B92AD5EABA98BD43084864C4D5483F191CD9 42 32 0.761905\n", + "6 00F7DCC41988D8642C51D4F8BA5A42C413275885 40 40 1.000000\n", + "7 6C699C8D1F7157366994ACDA5495051F2C58D7AB 38 38 1.000000\n", + "8 5BFC55F389F4B5427341E4320A501711140AE444 36 24 0.666667\n", + "9 699512C8B49E7A01A622BD250544E09A80A42D55 33 26 0.787879" ] }, - "execution_count": 113, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1348,7 +1456,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1382,72 +1490,72 @@ " \n", " 0\n", " 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81\n", - " 162\n", - " 122\n", - " 0.753086\n", - " \n", - " \n", - " 1\n", - " 28EAC321D548B4247D9C84810C0656EC9426716B\n", - " 97\n", + " 111\n", " 80\n", - " 0.824742\n", + " 0.720721\n", " \n", " \n", - " 2\n", + " 1\n", " F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284\n", " 71\n", " 71\n", " 1.000000\n", " \n", " \n", - " 3\n", + " 2\n", " 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03\n", - " 67\n", - " 67\n", + " 55\n", + " 55\n", " 1.000000\n", " \n", " \n", + " 3\n", + " 28EAC321D548B4247D9C84810C0656EC9426716B\n", + " 54\n", + " 39\n", + " 0.722222\n", + " \n", + " \n", " 4\n", - " 00F7DCC41988D8642C51D4F8BA5A42C413275885\n", - " 58\n", - " 58\n", + " 45A195BE1E17B3AFA086623DCC4661DEE2043B70\n", + " 51\n", + " 51\n", " 1.000000\n", " \n", " \n", " 5\n", " F243B92AD5EABA98BD43084864C4D5483F191CD9\n", - " 52\n", - " 45\n", - " 0.865385\n", + " 42\n", + " 32\n", + " 0.761905\n", " \n", " \n", " 6\n", - " 45A195BE1E17B3AFA086623DCC4661DEE2043B70\n", - " 45\n", - " 45\n", + " 00F7DCC41988D8642C51D4F8BA5A42C413275885\n", + " 40\n", + " 40\n", " 1.000000\n", " \n", " \n", " 7\n", " 6C699C8D1F7157366994ACDA5495051F2C58D7AB\n", - " 44\n", - " 44\n", + " 38\n", + " 38\n", " 1.000000\n", " \n", " \n", " 8\n", - " 699512C8B49E7A01A622BD250544E09A80A42D55\n", - " 39\n", - " 35\n", - " 0.897436\n", + " 5BFC55F389F4B5427341E4320A501711140AE444\n", + " 36\n", + " 24\n", + " 0.666667\n", " \n", " \n", " 9\n", - " 5BFC55F389F4B5427341E4320A501711140AE444\n", - " 35\n", - " 17\n", - " 0.485714\n", + " 699512C8B49E7A01A622BD250544E09A80A42D55\n", + " 33\n", + " 27\n", + " 0.818182\n", " \n", " \n", "\n", @@ -1455,19 +1563,19 @@ ], "text/plain": [ " ThumbPrint N correct score\n", - "0 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81 162 122 0.753086\n", - "1 28EAC321D548B4247D9C84810C0656EC9426716B 97 80 0.824742\n", - "2 F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284 71 71 1.000000\n", - "3 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03 67 67 1.000000\n", - "4 00F7DCC41988D8642C51D4F8BA5A42C413275885 58 58 1.000000\n", - "5 F243B92AD5EABA98BD43084864C4D5483F191CD9 52 45 0.865385\n", - "6 45A195BE1E17B3AFA086623DCC4661DEE2043B70 45 45 1.000000\n", - "7 6C699C8D1F7157366994ACDA5495051F2C58D7AB 44 44 1.000000\n", - "8 699512C8B49E7A01A622BD250544E09A80A42D55 39 35 0.897436\n", - "9 5BFC55F389F4B5427341E4320A501711140AE444 35 17 0.485714" + "0 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81 111 80 0.720721\n", + "1 F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284 71 71 1.000000\n", + "2 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03 55 55 1.000000\n", + "3 28EAC321D548B4247D9C84810C0656EC9426716B 54 39 0.722222\n", + "4 45A195BE1E17B3AFA086623DCC4661DEE2043B70 51 51 1.000000\n", + "5 F243B92AD5EABA98BD43084864C4D5483F191CD9 42 32 0.761905\n", + "6 00F7DCC41988D8642C51D4F8BA5A42C413275885 40 40 1.000000\n", + "7 6C699C8D1F7157366994ACDA5495051F2C58D7AB 38 38 1.000000\n", + "8 5BFC55F389F4B5427341E4320A501711140AE444 36 24 0.666667\n", + "9 699512C8B49E7A01A622BD250544E09A80A42D55 33 27 0.818182" ] }, - "execution_count": 114, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1521,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -2740,32 +2848,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "# Train test split\n", - "X_train, X_test, y_train, y_test = train_test_split(X, X['label'], \n", - " test_size = 0.2, \n", - " random_state = 123, \n", - " stratify = X['label'])\n", - "\n", - "X_train_wo_sensor, X_test_wo_sensor, y_train_wo_sensor, y_test_wo_sensor = train_test_split(X_wo_sensors, X_wo_sensors['label'], \n", - " test_size = 0.2, \n", - " random_state = 123, \n", - " stratify = X_wo_sensors['label'])" + "def set_label_ratio(X,thres):\n", + " X_tp=X\n", + " X_tp['label']=0\n", + " X_tp.loc[X_tp['proportion']>=thres,'label']=1\n", + " print(sum(X_tp['label'])/X_tp.shape[0])\n", + " return X_tp" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ - "def set_label(X,thres):\n", - " X_tp=X\n", + "def set_label_count(X,thres):\n", + " X_tp=X.copy()\n", " X_tp['label']=0\n", - " X_tp.loc[X_tp['proportion']>=thres,'label']=1\n", + " X_tp.loc[X_tp['malwareNum']>=thres,'label']=1\n", " print(sum(X_tp['label'])/X_tp.shape[0])\n", " return X_tp" ] @@ -2838,22 +2942,90 @@ "source": [ "for thres in list(np.arange(0.05,0.36,0.02)):\n", " print('------thres=',thres)\n", - " set_label(X_train,thres)\n", - " set_label(X_test,thres)" + " set_label_ratio(X_train,thres)\n", + " set_label_ratio(X_test,thres)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------thres= 2\n", + "0.7994616419919246\n", + "0.8031390134529148\n", + "------thres= 3\n", + "0.7558322117541498\n", + "0.7488789237668162\n", + "------thres= 4\n", + "0.730820995962315\n", + "0.7260089686098655\n", + "------thres= 5\n", + "0.7161283086585913\n", + "0.7107623318385651\n", + "------thres= 6\n", + "0.7028936742934051\n", + "0.6995515695067265\n", + "------thres= 7\n", + "0.6882009869896815\n", + "0.6860986547085202\n", + "------thres= 8\n", + "0.677321668909825\n", + "0.6775784753363229\n", + "------thres= 9\n", + "0.6609466128308659\n", + "0.6614349775784754\n", + "------thres= 10\n", + "0.6325706594885598\n", + "0.6358744394618834\n", + "------thres= 11\n", + "0.5944369672498878\n", + "0.5968609865470852\n", + "------thres= 12\n", + "0.5503589053387169\n", + "0.5506726457399103\n", + "------thres= 13\n", + "0.5112157918349035\n", + "0.515695067264574\n", + "------thres= 14\n", + "0.4669134140870345\n", + "0.47399103139013454\n", + "------thres= 15\n", + "0.4205921938088829\n", + "0.41838565022421526\n" + ] + } + ], + "source": [ + "for thres in list(range(2,16,1)):\n", + " print('------thres=',thres)\n", + " set_label_count(X_train,thres)\n", + " set_label_count(X_test,thres)" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ - "def compute_metric_thres(X_train,X_test,thres):\n", - " X_train_tp=set_label(X_train,thres)\n", - " X_test_tp=set_label(X_test,thres)\n", + "def compute_metric_thres(X_train,X_test,thres,drop_lst=[],mode=1):\n", + " if mode==1:\n", + " X_train_tp=set_label_ratio(X_train,thres)\n", + " X_test_tp=set_label_ratio(X_test,thres)\n", + " else:\n", + " X_train_tp=set_label_count(X_train,thres)\n", + " X_test_tp=set_label_count(X_test,thres)\n", " X_train_resample_tp,y_train_resample_tp=resample(X_train_tp)\n", " \n", - " model_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample_tp,y_train_resample_tp,X_test_tp.drop(['proportion','label'],axis=1),X_test_tp['label'])\n", + "# model_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample_tp,y_train_resample_tp,X_test_tp.drop(['proportion','label'],axis=1),X_test_tp['label'])\n", + " model_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample_tp.drop(drop_lst,axis=1),y_train_resample_tp,X_test_tp.drop(drop_lst,axis=1),X_test_tp['label'])\n", + " \n", + "\n", " score=balanced_accuracy_score(X_test_tp['label'], np.argmax(y_pred_with_sensor,axis=1))\n", "# print('Balanced Average Accuracy (including sensor features):', score)\n", " metrics=precision_recall_fscore_support(X_test_tp['label'], np.argmax(y_pred_with_sensor,axis=1))\n", @@ -2866,7 +3038,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -2874,105 +3046,105 @@ "output_type": "stream", "text": [ "------thres= 0.01\n", - "0.8087707492148946\n", - "0.8103139013452915\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8548519613114746 recall_benign=0.8037825059101655 recall_malware=0.9059214167127836\n", + "0.7897771952817825\n", + "0.789832285115304\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.886871242596892 recall_benign=0.8553615960099751 recall_malware=0.9183808891838089\n", "------thres= 0.03\n", - "0.7994616419919246\n", - "0.8031390134529148\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8591241451499461 recall_benign=0.8109339407744874 recall_malware=0.9073143495254048\n", + "0.7896461336828309\n", + "0.789308176100629\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.873178926086698 recall_benign=0.8233830845771144 recall_malware=0.9229747675962815\n", "------thres= 0.049999999999999996\n", - "0.7520188425302826\n", - "0.7457399103139013\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8590412134208486 recall_benign=0.8112874779541446 recall_malware=0.9067949488875526\n", + "0.7870249017038008\n", + "0.7861635220125787\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8808921568627451 recall_benign=0.8357843137254902 recall_malware=0.926\n", "------thres= 0.06999999999999999\n", - "0.7239793629430238\n", - "0.7197309417040358\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.861582554517134 recall_benign=0.816 recall_malware=0.907165109034268\n", + "0.7815203145478374\n", + "0.7809224318658281\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8766706271474904 recall_benign=0.8325358851674641 recall_malware=0.9208053691275168\n", "------thres= 0.08999999999999998\n", - "0.7095109914759982\n", - "0.7053811659192825\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8591654643958504 recall_benign=0.806697108066971 recall_malware=0.9116338207247299\n", + "0.7745740498034076\n", + "0.7772536687631028\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8761699258260283 recall_benign=0.84 recall_malware=0.9123398516520567\n", "------thres= 0.10999999999999997\n", - "0.6970614625392553\n", - "0.6937219730941704\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8562456405019492 recall_benign=0.8023426061493412 recall_malware=0.9101486748545572\n", + "0.7623853211009174\n", + "0.760482180293501\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8553566769767171 recall_benign=0.8030634573304157 recall_malware=0.9076498966230186\n", "------thres= 0.12999999999999998\n", - "0.6791161956034096\n", - "0.6784753363228699\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8541616543190075 recall_benign=0.793584379358438 recall_malware=0.914738929279577\n", + "0.7428571428571429\n", + "0.7389937106918238\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.842805263607622 recall_benign=0.7891566265060241 recall_malware=0.8964539007092198\n", "------thres= 0.15\n", - "0.65814266487214\n", - "0.657847533632287\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8541209358173393 recall_benign=0.7798165137614679 recall_malware=0.9284253578732107\n", + "0.7193971166448231\n", + "0.720125786163522\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8223427048067644 recall_benign=0.7640449438202247 recall_malware=0.8806404657933042\n", "------thres= 0.16999999999999998\n", - "0.6087931807985644\n", - "0.6062780269058295\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8232509670984351 recall_benign=0.744874715261959 recall_malware=0.9016272189349113\n", + "0.6657929226736566\n", + "0.6645702306079665\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8057940654574133 recall_benign=0.7921875 recall_malware=0.8194006309148265\n", "------thres= 0.18999999999999997\n", - "0.5584342754598475\n", - "0.5569506726457399\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.843597403952095 recall_benign=0.8208502024291497 recall_malware=0.8663446054750402\n", + "0.608781127129751\n", + "0.6158280922431866\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8135816086615772 recall_benign=0.7803547066848567 recall_malware=0.8468085106382979\n", "------thres= 0.20999999999999996\n", - "0.5163750560789592\n", - "0.5224215246636771\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8451449757198413 recall_benign=0.8018779342723005 recall_malware=0.8884120171673819\n", + "0.5636959370904325\n", + "0.5718029350104822\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8322550028215723 recall_benign=0.7882496940024479 recall_malware=0.8762603116406966\n", "------thres= 0.22999999999999998\n", - "0.4728577837595334\n", - "0.4780269058295964\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8423894121970561 recall_benign=0.7757731958762887 recall_malware=0.9090056285178236\n", + "0.5174311926605505\n", + "0.5178197064989518\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8099432318253829 recall_benign=0.7271739130434782 recall_malware=0.8927125506072875\n", "------thres= 0.24999999999999997\n", - "0.4329295648272768\n", - "0.437219730941704\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8252242312800082 recall_benign=0.7314741035856573 recall_malware=0.918974358974359\n", + "0.4748361730013106\n", + "0.470125786163522\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7843355199825333 recall_benign=0.6745796241345203 recall_malware=0.8940914158305463\n", "------thres= 0.26999999999999996\n", - "0.3566621803499327\n", - "0.34798206278026905\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.7929662573207221 recall_benign=0.6568088033012379 recall_malware=0.9291237113402062\n", + "0.3871559633027523\n", + "0.3731656184486373\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7480341775957311 recall_benign=0.6070234113712375 recall_malware=0.8890449438202247\n", "------thres= 0.29\n", - "0.2711978465679677\n", - "0.26905829596412556\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.7805163599182003 recall_benign=0.8226993865030675 recall_malware=0.7383333333333333\n", + "0.2916120576671035\n", + "0.28354297693920333\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7438364295981188 recall_benign=0.8222384784198976 recall_malware=0.6654343807763401\n", "------thres= 0.30999999999999994\n", - "0.20592193808882908\n", - "0.20134529147982064\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8147127123847492 recall_benign=0.8298708590679393 recall_malware=0.799554565701559\n", + "0.21874180865006554\n", + "0.21016771488469602\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7706869190659715 recall_benign=0.8181818181818182 recall_malware=0.7231920199501247\n", "------thres= 0.32999999999999996\n", - "0.1594885598923284\n", - "0.16322869955156952\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8288841383696689 recall_benign=0.8665594855305466 recall_malware=0.7912087912087912\n", + "0.17116644823066843\n", + "0.15828092243186584\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7932545998861884 recall_benign=0.8580323785803238 recall_malware=0.7284768211920529\n", "------thres= 0.35\n", - "0.1320098698968147\n", - "0.13632286995515694\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8398764141662568 recall_benign=0.8738317757009346 recall_malware=0.805921052631579\n", + "0.1436435124508519\n", + "0.1278825995807128\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8015989517654476 recall_benign=0.8695913461538461 recall_malware=0.7336065573770492\n", "------thres= 0.36999999999999994\n", - "0.1078959174517721\n", - "0.10582959641255606\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8452327320945888 recall_benign=0.8811434302908726 recall_malware=0.809322033898305\n", + "0.11651376146788991\n", + "0.1090146750524109\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8080316742081448 recall_benign=0.8852941176470588 recall_malware=0.7307692307692307\n", "------thres= 0.38999999999999996\n", - "0.08770749214894571\n", - "0.08475336322869956\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8474863188238984 recall_benign=0.8907398334149926 recall_malware=0.8042328042328042\n" + "0.09541284403669725\n", + "0.09014675052410902\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8019169971064195 recall_benign=0.9003456221198156 recall_malware=0.7034883720930233\n" ] } ], @@ -2981,7 +3153,239 @@ "pd_metric=collections.defaultdict(list)\n", "for thres in list(np.arange(0.01,0.40,0.02)):\n", " print('------thres=',thres)\n", - " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train,X_test,thres)\n", + " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train.drop('malwareNum',axis=1),X_test.drop('malwareNum',axis=1),thres,['proportion','label'],1)\n", + " pd_metric['thres'].append(thres)\n", + " pd_metric['balanced_accuracy'].append(score)\n", + " pd_metric['benign_recall'].append(recall1)\n", + " pd_metric['malware_recall'].append(recall2)\n", + " pd_metric['benign_precision'].append(precision1)\n", + " pd_metric['malware_precision'].append(precision2)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(pd_metric).to_csv('../../xgboost_threshold.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.DataFrame(pd_metric)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", + "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", + "plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", + "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", + "# plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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6+lfmH5pPZl4mj7R+hAlBEwxXBKxQZl4m03ZMY9P5TfTz6se7Xd7FxsRCq/q45wu4sBcs7aDdKAh5GhrosQKlgX2w7wNWRK1g9yO7MTOpXaOplJpPdblUUIFOsjo8nvmbT3MuKZOgJg683KclYS2ca01iT8lOYcHhBfwa9StO1k7M6DqDbu7dDLLvuPQ4Jm2ZRExqDC91eInH/B777/cWd1BL7JErAQmt7tVqtnt2rrWlfW/448wfTN0+lRWDVuDr6GvscJQ6RiX0Ssor0PHrwTgWbD7NxbRsOnk58mhoU0KbOeFiWzsugIlIjODNnW8SnRrNUwFP8UzbZ/Taatx3aR8v//MyBbKAud3n0sW9S9krpMXDvsVwcAlkp0LjdtD5WWgzpNZOrXfu2jnuXXkv00Onc3/L+40djlKDSCnZdjoRT0cbvJ0rVw9IJfQqyskvYPn+C3z2dzRX03MAaOlany7NnQlt7kRnbyfsbWpu8snKz2L2vtn8dvo3Orh2YE73OWXWdq8MKSXLTy1n9r7ZNLVryoJeCyo2kUduBhz5SetbT4rWrky9awoEP16tcRqClJKuP3elb9O+TO8y3djhKDXE4fMpfLDhJHvOJPNoZ0/eGxJQqe2ohF5N8gt0RFy8xu6YJHbFJLI/NpnsPB1CQJvGdkUJvqOXI/Uta17f6e8xvzNjzwyszayZ1W3W7VvP5ZRXkMf7+95nRdQKunt054OwDyrfZ6/TQfRGrfzAhb0w7h/tqtRa5umNT5Ocncwvg34xdiiKkUVfTefDP0/xZ+QVnOpZ8FwvHx4JaVrpa19UQteT3Hwd4RdSixL84fOp5BboMDMRBDVxILSZE12aO9G+aYMac0XqmdQzvPzPy8SkxvBU4FNMCJpQpS6YpKwkXtr6EoeuHuJJ/yd5rt1z1TO+PDsN5rcF1zYw+vda16++4NACvon4ht2P7FbFuu5QF1OzmL/pNL8cvIC1uSnjujfnyTDvKjf2VEI3kKzcAg6eS2FXTCK7zyRxNC6NAp3EwsyEDp4N6OPnyohOnlhbGDe5Z+VnMWvvLFZGryTYNZg53efgYuNS4e2cTD7JpL8nkZydzLtd3mVgs4FVjk2nk+yPTeb3oxfpeGUFgy99AiN+Bt/adZHOlvNbmLRlEt8N+I52DdsZOxzFgFIycln4TwxLdsWChEc7N+XZns1xql89591UQjeS9Ow89scmsys6iZ0xSZy4dK2ojszIkKZGT+xrYtbw3p73tC6YsFl0aVz+Lpg/Y//kjR1vYG9pz/xe82njVPl6LFJKjsalsebIRf44eonL17KxMjfBROazzmwyzvWtqP/i/lp1kjQhM4Fev/Ti1Y6vMspvlLHDUQwgMzefb3fGsuifGK7n5DOsnQcv3N2CJo7Ve+W2Sug1xL6zyczfHMXO6CSc61sy/q5mRi8QFpMaw8tbX+ZM2hnGBY5jQtCEMrtMdFLH5+Gfs/joYtq6tGVez3k4WztXeL9SSk5dSef3Ixf5/cglzidnYm4quKtlQwYFuXF3a1cupmax/IdFvJH+HitcX6DPmDewt649Sf3uX+6mvWt75nSfY+xQFD3KK9Dx8/4LLNh8moT0HO5u7crkfr74NrLVy/5UQq9h9p5JYv5mrUCYi61lYYvd02iJPTMvk/f3vs/qmNV0atSJ2WGzS+yCycjL4LXtr7HlwhaG+gzljc5vYGFqUaF9nU3MKEziFzl99TqmJoIuzZ0YFNSYfn6N/jNaKC+/gCsLemOdFs3Dll8wfXgXuvpU/D8QY3hxy4ucSjnFumHrjB2Kogc6nWTtsUt89NcpziVl0tGrAVP6tyLYy1Gv+1UJvYbacyaJ+ZtOs/tMEg1tLZnQozkjOhkvsa+KXsXMPTOpZ16P2d1n09mtc9F7F65dYNKWSZxNO8vkjpN5pNUj5b7IKj41iz+Oai3xY/FpAHTydmRQUGMG+DfC+XZ9i/GH4P96ssz8fl5Pv58xXbyYOqBVjTnRXJqvj33NJ4c+YcfDO7C3tDd2OEoVSSlJz8knNSOPqCvpzNsUReTFa7RqZMur/X3p6WuYK8pVQq/hdsck8cmmKPaeTaahrSXP9GjOw0ZK7NEp0bzyzyucSTvDhKAJjAscx/4r+3nln1eQUjL3rrnlmu9Tp5P8djien/ed58C5FACCmjgwKNCNewLdcLOv4MiPX59CHl/NJ61/Yv6BbJq71OPj4W0JauJQiU9pGHsv7WXsX2NZdPciurp3NXY4SjFSSlIy80jOyCElM4+UjFxSM/NIzswlJTOX1Iw87T5Tu0/JzCM1M5f8YvWdPBpY81Kflgxu627QYn4qodcSu2IS+WTjafbFJuNqZ8kzPXx4qGMTgyf2zLxMZu6dyZqYNbR2bE1UShRedl4s6LWgXFPfHbmQylurIzgSl0ZL1/oMbuvOoMDGeDpV4eRQ6gX4tAP4DWZH4CwmrzjC1fQcJvb0YWIvH8xrYNnj9Nx0uv7UlWfaPsP4oPHGDueOl5aVx+6YRLadTmT76QQuJGeVuJy5qaCBjQUNbCxwsDHXHtczx8HGggaFz51tLenS3AlLM8M3ulRCr0WklOyOSWLepij2x6bQyM6KZ3o256GOTUo8eKSU6KRWf0YnZdG9TgcFhc/NTQUONhXr64Z/u2A6u3VmVtis214slJyRy5wNJ1l+4ALO9S2ZNrA1g9s2rr6foZumw4558NQW0hwDmL4mkpWH4wn0sOfj4W3xaWjYAmTlMXjVYDxtPfm096fGDuWOk1+g40hcKtuitAQefiEVnYR6FqaENnemczNHXGwtb07e9SyoZ2Fao2s2qYReC0kp2RWTxLyN2iQcVuYmmJmYUKCTFEiJLEre5dteVx8nHu/iTa9WDTGpwM/DrPwsrEytyjzAC3SSZXvPMfevKDJy8nm8qxeTerfA1qqaR6RkX4MF7cClFYxZC0Kw7tglpq08RmZuAVP6t2JMF68KfT59m7ZjGjvjd7Jl+JYanSTqivNJmWw7ncD20wnsikkiPTsfISDQw4HuLZwJa+FCO0+HGvmLrrzKSug17/p0BdBqtXf1caZLcyd2Riex+eQVBAITAaYmAhMTgan4997UhGKPBSY37k0ESddzWL7/AmO/O4CXkw2ju3jxYHCTcl2xdrurHA+eS+bNVZEcv3SNLs2deOe+NrRw1c9wLazsoMdUWPcKnFoHre5hYIAbwU0bMPW3Y7y79jibTlzhwweDcHeoGVdn+jv7syZmDZczLuNW383Y4VS77LwCtp5KoKGdJQHu9gZPlOnZeeyOSWL76US2nU4omhy+sb0V9wS4EdbCha4+TpX6hVobqRb6HSKvQMefkZf5ZsdZDp1PxdbSjOEdmzA61KtSfdsJ6TnMXn+SXw/F4WZvxRv3+DEwoJH+W6EFebCwC0gdPLOn6GIjKSXL919gxtrjmAjB9PvaMKy9u9FbxRGJEYz4YwQf9/iYPk37GDWW6nQhOZMf9pxj+YELpGZq88lam5vSoWkDOnk70snbkbZNHKr1/I9OJzmTmMGRC6kciUvlyIVUIi5eo0AnsbEwJbSZE2EtnAlr6UIz53pG/9vrS5W7XIQQ/YH5gCnwlZRy9i3v2wM/AJ5orf65Uspvy9qmSujGE34hlW93nuWPo5cokJI+rV15ops3Id6Ot/1HkF+g47vd55i3MYrs/AKeCmvGsz19qGfIYmSn1sNPD8OADyFk3E1vnU/K5OVfwtkfm8I9gW68PzTAqBcj5RbkErIshFF+o3ipw0tGi6M66HSSHdGJfLc7ls0nr2IiBH0Ly1lk5OSz92wye88mc/LyNaQEC1MT2jZxIKSZluDbezao0HFy9Vo24YXJO/xCKkcvpJGekw9o/eABHva092xAWAsX2jd1MMoJSmOoUkIXQpgCUUAfIA7YD4yQUh4vtszrgL2UcooQwgU4BTSSUuaWtl2V0I3vclo2P+w5x497z5GSmUdrNzse7+rFfUGNS2xZ7TmTxNurIzl1JZ3uLV2YPsiPZi5GOBEpJSwdBFci4flwsLp5jHeBTrLonxjmbYzC1c6KeQ+1pZO3fi/2KMuItSOwNrfmm37fGC2GqriWnceKA3F8v+ccZxMzcK5vwYhOnjwS4lni8NO0TK3kxd6zSew7m1zUijYzEfi72xNS2IIP9nIs+s/2ek4+x+LStOR9Xkvil9KyAa2LsVUjW4KaONC28NbcpX6tmve3OlU1oYcC06WU/QqfvwYgpZxVbJnXgCbAs4AXsBFoKaXUlbZdldBrjuy8AlYdjufbnbGcupKOUz0LRnZuyqOdPWloa8WVa9nM/OMEa45cxN3BmrcG+dHXz9W4P2kvhsPiHtB1EvR5t8RFwi+k8vzPh7mQnMnEnj5M6t0CMyOcDJu5Zya/n/mdnQ/vrJ5KlAZy6nI63+2OZeXheDJzC2jn6cDoUC8GBDSqUGv4ek4+B8+lsO9sEnvPJHMkLpW8AokQ0LqRHfk6HaevXudGKmrqZEOQh0NhArenTWP7Gn8RmSFVNaE/APSXUo4tfD4KCJFSTiy2jC2wBmgF2AIPSSn/KGFb44BxAJ6enh3OnTtXuU+k6MWNkTXf7jzL5pNXMTPRJtHeFZ1Ink4y/q7mTLirudGLihVZOR4ifoOJ+0udk/R6Tj5vr47k10NxtPN0YP5D7ao2Hr4SVkev5o2db7Bq8CqaOzQ36L4rKr9Ax8bjV1i6O5Y9Z5KxMDPhvqDGPBbalEAPh2rZR3ZeAYfOp7DvbDL7Y5MxL+yaCWriQJCHA4717owTmJVV1YT+INDvloTeSUr5XLFlHgC6Ai8BzdFa6EFSymulbVe10Gu2s4kZLN0Vy+rweDo0bcCb9/rR1KlyU2bpTVqcdrFR60Fw/1dlLvr7kYu8vvIYUsKMIW0Y2s7DQEFqNegHrx7MjK4zGOIzxGD7rYiE9Bx+3neeZfvOcyktG3cHa0aFNmV4cBOVYGuYqg5bjEPrTrnBA7h4yzKPA7Ol9r9DtBDiLFprfV8l4lVqAG/neky/rw3T76t8WVy9s/eA0ImwfS6ETACPDqUuOiioMe08HXhxeTgvLj/C1lMJzBjij111j5UvgZe9F/XM6xGRGFGjEnpCeg4bj1/hz8jL7IpJJK9AEtbCmXcH+9OrVcM7to+6NitPQt8PtBBCeAPxwMPAI7cscx7oDWwXQrgCvsCZ6gxUUUrU7QU4tBT+egMeX1fmzEYeDWz46anOfLE1hvmbT3PwXArzH25Lh6b6PWFqIkxo49SGiMQIve6nPM4lZfBn5GX+jLzCofMpSAmejjY83tWb4cFNauTVtkr53TahSynzhRATgT/Rhi1+I6WMFEKML3x/ETADWCKEOAYIYIqUMlGPcSuKxtIWer4Oa1+Ek2u17pcymJmaMKl3C7r6OPPC8sMM/3IPz/XyYWJPH72eMPV39ue749+RW5Bb4ZLDVSGl5Pila/wZeYW/Ii9z8nI6AH5udrzQuyX9/F3xdbWts2O27zTqwiKl9ivI1y420uXBM3vBrHwJMz07j7dWa/Vggps2YN5Dbat9dpkbNp3bxItbX2TZwGUEuFRutvfyKtBJDsQma0n8+GXiUrIwERDs5UhfP1f6tWmkt8+p6J+69F+p20zNoO8MWDYcDn4LIU+XazVbK3PmPdSWu1q68MaqCAbO3857Q/0Z3Na92kP0d/YH4FjiMb0k9LwCHdtPJ/BnxBU2nbhCUkYuFqYmdGvhzHO9fOjd2vX2decVg0jNTsXO0g4TUf2/CFVCV+qGFn3B+y7YOhsCHwJrh3KvOqSdO+09G/DC8sM8/3M4/5xK4N0h/lWenb04VxtXnK2d9dKPfvpKOi/+L5yI+GvYWprRs1VD+rVpxF2+LtX6GZSqO5N6hvGbxjOo+SCea/fc7VeoIPXXVuoGIaDve/Bld9j+kdZirwBPJxv+93QoC/6O5rO/T3PwfAoLHm5XbRNoCCHwd/YnIqn6ErpOJ/l2VywfbDhJfUsz5j/clv7+FbvoRzGcQ1cO8dzfz2FuYk4vz1562UftrSGpKLdyC4S2j8DeRZASW+HVzUxNeKlPS34eF0pevo77F+5i4dYYdOWtUXwb/k7+nE07S3puepW3FZ+axaNf72XG2uOE+Tiz4YUwBrd1V8m8htp4biNP/fUUjlaO/DDwB9o46Wc4sEroSt3S6w0QprC55HIA5dHJ25H1z3enj58rH2w4yWPf7OPqtewqhxbgrPWdRyZFVnobUkpWHo6j/yfbCL+QyuxhAXw1OpiGtlZVjk/Rjx9P/MjLW1+mtVNrvhvwHR62+ruoTSV0pW6xawxdnoOIXyH+YKU3Y29jzhcj2zNrWAAHziXTf/52Np+4UqXQ2jhrrbLK9qOnZOTy7LJDvLj8CL6utqx/PoyHO3mqIYc1lE7q+OjAR8zeN5ueTXryVd+vaGDVQK/7VAldqXu6PAfWjvD3e1XajBCCEZ08WftcN1ztrHhy6QGmr4kkO6+gUtuzt7TH09azUgl9y6mr9P1kGxuPX+HV/r4sfzq05pViUIrkFuQyddtUlkQu4WHfh/m4x8dYmen/V5RK6ErdY2UHYS9BzN8Qu6PKm/NpaMvKZ7owposXS3bFMuTznURfrVw/uL+zP8cSj5V7+YycfF5feYzHv92Po40Fq57tyjM9fNRl+TXYtdxrjN80nvWx63mh/Qu8HvK6wapsqoSu1E0dx4KtG2yeAdVw8ZyVuSnT72vDN2OCuZqew72f7uCnfeep6IV5Ac4BXM28ytXMq7dd9uC5FAYu2M5P+84zrnszVk/sSpvG9rddz5ByC3JZe2YtVzKq1h1VV1zOuMzo9aM5fPUws8Jm8WTAkwbtElMJXambzK3hrlfhwh44vbHaNturlSsbng8juKkjr/12jGd+PERa4RRs5XHjAqOyul1y83XM/fMUDy7aRX6B5KenOvP6wNY1qia4lJK/Yv/ivlX38dr21xi+djh7L+01dlhGFZUSxch1I7mccZmFdy/k3mb3GjwGldCVuqvdKGjgBX+/C7pS51qpsIZ2Vnz3RCemDmjFxuNXGDB/G/vOJpdr3VaOrTAVpqUm9NNX0hn6xU4+2xLNsPYebHghjM7NnKot9uoQkRjBmA1jePmfl7E2s+b9bu/jYOnAuI3j+OrYVxX+1VIX7Lu0j9HrR4OEJf2X0Nmts1HiUBcWKXWXqTn0eB1WjoPjq8B/WLVt2sREMP6u5oQ2c2LSz4d5ePFunuvVgud6lV3kywQLvO182BMfTjvbBJKu55KUkUtyRg5XruWw5shF6luasejRDvT3b1Rt8VaHyxmXWXBoAb+f+R1HK0feCn2LYT7DMDUxpbdnb97e9TbzD83naMJRZnabia2FrbFDNoh1Z9Yxbec0mto2ZeHdC3Gr72a0WFRxLqVu0xXAwq7/Fu4yrf42zPWcfN5aHcFvh7QiXwMD3EjOyCUpI6dYws4l6XoO17LzsWz0G+Z2R7ke9RY3fiSbmQga1LOgk7cj0we1wcW25tRdyczL5NvIb1kSsQSd1DHKbxRjA8ZS3+LmUrtSSn488SMfHfiIxvUb83GPj/F19DVS1PonpWRJ5BI+PvgxHVw7ML/nfOwt9X+Oo0ozFumLSuiKwZxYC8tHwuDPod2jetvN6vB4pq2M4HpOPiYCHOtZ4lTPAqf6FjjWs8C5viWO9SyIy9/K+ssLmBn8A20aNsepngV2VuaY1LCRKzqpY03MGhYcWkBCVgL9vfrzQocXcK9fdvGyw1cP88rWV7iWe423Qt9iUPOySxpXhpSS8IRwjiYcRUqJRKKTOiQSKW9+fOM9XeEUxzfeMxEm2FrYYm9hj72lPXYWdthb/vvY2sy61BOaBboC5uyfw7KTy+jn1Y+Z3WZiaWqY/4RVQlfubFLC//WCjAR47iCY6e8fXkZOPrn5OuytS0/QUSlR3L/mfmaFzaq2E2cp2SnsvbQXTztPvO29sTazrtL29l/ez4f7P+RE8gkCnQOZ3HEybRu2Lff6iVmJvLrtVfZf3s9Dvg/xasdXq6UOfL4un03nNrE0cmmF6uIIBEIITDABASaYoJM68mV+qeuYmZiVmuzPpp1l58WdjPIbxSvBr+ilcmKpn0WVz1XuaEJA77fg+yFwcEm5y+tWRj1LM+rd5v+LZvbNsDazJiIxoloSupSS13e8zo54bcy9QOBe3x0fBx+aOTTDx8GH5g7Ny5Xoz187z8cHP2bz+c00qteID8I+oL93/wonLGdrZxb3WcyCQwv4NvJbjicd56O7Pqp0/3J6bjq/nf6NH0/8yKWMSzS1a8obIW/Q16svFqYW/yZsYXJT0hZCFL13KyklWflZXMu9RlpOWtH9TY9z07iWc4203DSuZl4lOjWatJw08nR5TA6ezGNtHqvU59EXldCVO0OzHuAVBts+1LpdLIx3laWZiRmtHVtX6AKjsvx9/m92xO9gXOA4fBv4EpMWQ0yqdttxcQf5Oq0VeiPRN3doTnOH5kUJv5l9M3ILcvny6Jf8dPInLEwsmNRuEqP8RlXp6kYzEzNeCn6JQJdA3tj5BsPXDueD7h/QpXGXcm/j4vWL/HjiR349/SsZeRkEuwbzWqfXuKvJXVVuFQshsDG3wcbchkb1KnYCWkpZI0suqISu3BmEgF5vwjd9tWqMYS8bNZwA5wB+OvkTebo8zE0qP1F1Zl4ms/fPpkWDFkwImoCZyc3/pPN0eVy4doGYtBiiU6OLEv3OiztvSvQWphbkFuQyrMUwJrabiLO1c5U+X3F3N70bHwcfXtz6IuM3jmdiu4mMDRhbZkI+lnCMpceXsuncJgD6efXjMb/HiurhGFtNTOagErpyJ/EMgZb9Yed8CH6yQpNgVDd/Z39ydbmcTjmNn5Nfpbez+OhiLmdc5oOwD/6TzAHMTcy1VrhDM/o07VP0ep4ujwvpF4hJ1RJ9UlYSD7Z8UG+jUrzsvfhx4I+8s/sdPj38adHQxuKjQgp0BWy9sJXvjn/HoauHqG9en1F+oxjZemSFW9B3KpXQlTtLrzdgUTfY9Sn0ftNoYRS/YrSyCf1M6hmWRi5lcPPBtHdtX6F1zU3MaWavdbcUT/T6ZGNuw+yw2bRt2JY5++fw8NqHmddzHp62nqyKXsUPJ37gQvoF3Ou7M6XjFIa2GEo9c1WArCJUQlfuLI0CwP9+2LNQOzlav6FRwnCv704DywZEJEYw3Hd4hdeXUjJz70ysza15scOLeohQP4QQjGg1Aj8nP17a+hKPrnsUC1ML0nPTCXQJ5IX2L9DLs1eJvzaU21PfmnLn6fE6RK6C7R/DgNlGCUEIQRvnNpU+Mbr+7Hr2Xd7HGyFv4GRds0oDlEeQSxD/u/d/fLD/A3RSx6OtH63QsEilZCqhK3ceZx9tqroDX0Pos+DQxChhBDgHsOviLjLzMrExtyn3etdzrzP3wFzaOLXhgZYP6DFC/XKydmJO9znGDqNOUcW5lDvTXVO0+38+MFoI/s7+6KSO40nHK7Te5+Gfk5iVyJud3zRYnW2ldlAJXbkzOTTRRrqEL4PEaKOEUJ5Surc6lXyKZSeX8WDLB2vMED6l5lAJXblzhb0EZlaw9X2j7N7RyhH3+u7l7kfXSR3v7XkPewt7JrWfpOfolNqoXAldCNFfCHFKCBEthJhawvuThRDhhbcIIUSBEMKx+sNVlGpUvyF0Hq9NKH25eq7arCh/Z38ikyLLtezq6NWEJ4TzYocXDVLVT6l9bpvQhRCmwOfAAMAPGCGEuGngrJTyQyllWyllW+A14B8pZfkq/iuKMXV5DqzsqzyhdGX5O/kTfz2epKykMpdLy0lj3sF5tGvYjsE+gw0UnVLblKeF3gmIllKekVLmAj8DZR1RI4CfqiM4RdE76wbQ9XmI2gDnDT+F2o1+9Nu10ucfms+13GtMC5lm0Mp+Su1SniPDHbhQ7Hlc4Wv/IYSwAfoDv5by/jghxAEhxIGEhISKxqoo+hEyHuq5wN/VM6F0Rfg5+WEiTMo8MRqRGMGKqBWMaDWiTk8YoVRdeRJ6SVVoSjvqBwE7S+tukVIullIGSymDXVxcyhujouiXRT3oPhlit8OZrQbdtY25Dc3sm5V6YrRAV8CMPTNwtnbm2bbPGjQ2pfYpT0KPA4pfeeEBXCxl2YdR3S1KbdRhDNg3gc3vGryVHuAcQERiRImTK6+IWsHxpONM7jj5P1O+KcqtypPQ9wMthBDeQggLtKS95taFhBD2wF3A6uoNUVEMwMxSu9jo4iE4+YdBd+3v7E9qTirx1+Nvej0pK4n5h+cT0iiE/l79DRqTUjvdNqFLKfOBicCfwAngf1LKSCHEeCHE+GKLDgX+klJm6CdURdGzoBHg5KONeNEVGGy3pV1g9PHBj8nKz+L1zq/X2PrbSs1SrtPlUsp1UsqWUsrmUsqZha8tklIuKrbMEinlw/oKVFH0ztQMek6DhBNwap3BdtuiQQssTCxu6kc/dOUQa2LWMNpvNM3smxksFqV2U+OfFKW41veBtSOc+N1guzQ3Mae1U+uiFnq+Lp/39r6HWz03xgWOM1gcSu2nErqiFGdqBi37QdSfUFD6jPDVzd/ZnxPJJ8jX5bPsxDJOp5xmSqcpFarCqCgqoSvKrXwHQHYqXNhjsF36O/uTlZ/Fnkt7+Dz8c8Lcw+jVpJfB9q/UDSqhK8qtmvcCUws4td5guwxwDgDg9e2vk6/L57VOr6kToUqFqYSuKLeytAXvu7ThiwYak+5p64mthS0pOSmMDRhLEzvjTLqh1G4qoStKSXwHQMpZSIwyyO6EEHRw7UBTu6Y8EfCEQfap1D0qoStKSVoWXshjwOGLs8Nms+yeZViaWhpsn0rdohK6opTE3h3c2hq0H72eeT3sLOwMtj+l7lEJXVFK4zsQLuyD66oyqFI7qISuKKXxHQBIOP2nsSNRlHJRCV1RStMoAOw84KTh+tEVpSpUQleU0gihtdJj/oa8LGNHoyi3pRK6opTFdwDkZ8GZf4wdiaLclkroilIWr25gYWvQ4YuKUlkqoStKWcwswae3Nom0TmfsaBSlTCqhK8rttLoHrl+Bi4eNHYmilEkldEW5HZ+7QZiqbhelxlMJXVFux8YRmnYx6FWjilIZKqErSnn4DoCrkZASa+xIFKVUKqErSnn4DtDuT20wbhyKUgaV0BWlPBybgUsr1Y+u1GgqoStKefkOgHM7ISvV2JEoSolUQleU8vIdCLp8iN5k7EgUpUQqoStKebl3gHouqttFqbFUQleU8jIxhZb94PQmyM81djSK8h8qoStKRfjeAzlpcH6XsSNRlP9QCV1RKqJZDzCzUhcZKTVSuRK6EKK/EOKUECJaCDG1lGV6CCHChRCRQghVa1SpmyxsoFlPrR9dSmNHoyg3uW1CF0KYAp8DAwA/YIQQwu+WZRyAL4D7pJRtgAerP1RFqSF8B0Dqebh63NiRKMpNytNC7wRESynPSClzgZ+Bwbcs8wjwm5TyPICU8mr1hqkoNUjL/tq9Gu2i1DDlSejuwIViz+MKXyuuJdBACLFVCHFQCPFYSRsSQowTQhwQQhxISFAzqSu1lK0ruAerfnSlxilPQhclvHZr56EZ0AG4B+gHvCmEaPmflaRcLKUMllIGu7i4VDhYRakxfAdA/EFIv2zsSBSlSHkSehzQpNhzD+BiCctskFJmSCkTgW1AUPWEqCg1kO9A7b46W+l5WZB9rfq2p9xxypPQ9wMthBDeQggL4GFgzS3LrAbChBBmQggbIAQ4Ub2hKkoN0rA1ODStvoSefhm+CIX/6wl52dWzTeWOc9uELqXMByYCf6Il6f9JKSOFEOOFEOMLlzkBbACOAvuAr6SUEfoLW1GMTAitlX5mK+RmVG1bGUnw3WAtqSdFw/aPqiVE5c5TrnHoUsp1UsqWUsrmUsqZha8tklIuKrbMh1JKPymlv5TyEz3Fqyg1h+8AKMiBmC2V30Z2GvwwVJs4Y+QvEDAcdsyDhKhqC1O5c6grRRWlspp2ASv7yne75GbAj8PhynEY/j14h0G/mdrFS3+8pC5cUipMJXRFqSxTc2jRF6I2gK6gYuvmZcPPj0DcPrj/K2jZV3u9fkO4ezrEbocjP1d7yErdphK6olSF7wDITIS4A+VfpyAPfhmj9b8P/hzaDLn5/fZjwKMT/DUNMpOrL1alzlMJXVGqwuduMDEr/1WjugJY+TRErYeBc6HtI/9dxsQE7p2nzYy06e1qDVep21RCV5SqsLIHr27l60fX6eD35yHiV61bpdNTpS/byB9Cn4VD38G53dUWrlK3qYSuKFXlOxAST0FSTOnLSAl/vg6Hv4ewV6Dbi7ffbo+pYN8E1r6oJtRQykUldEWpqqJiXWW00rfMhL0LIWQC9HqjfNu1qAcDP4SEE7D7s6rHqdR5KqErSlU1aAqu/qX3o++YB9s+hHajoP8s7aKk8vIdAK3uhX/maGPVFaUMKqErSnXwHQDnd/93VMq+/4NN08H/fhg0v2LJ/IYBH2jzma6brMamK2VSCV1RqoPvAJA6OP3Xv6+FL4N1r0DLATD0Sy0pV4a9B/R8Xdv28dXVE69SJ6mErijVwa0d2Lr92+0SuQpWPwved8GDS7SLkKqi09PQKAA2TFUVGZVSqYSuKNXBxEQ7ORq9GU78Dr8+CR4dYcRPYG5V9e2bmsG987UCXltmVn17Sp2kErqiVBffgZB7HZY/Cg394JH/aSNVqotHB+j4JOxbDBcPV992lTpDJXRFqS7e3cHSHpx9YdRKsHao/n30fgvqucDvL1S8foxS56mErijVxdwKxm+DpzZDPWf97MPKXhv6eClcG0GjKMWohK4o1amBF1ja6ncfbYZB817w93tw7dbZIJU7mUroilLbCAH3fAS6PG3Ui6IUUgldUWojx2bQ/RVtXHrUX7dfXrkjqISuKLVVl+e1E7DrXobcTGNHo9QAKqErSm1lZgH3fgyp52HbHGNHo9QAKqErSm3m1Q3ajoRdn2pzkyp3NDNjB1BcXl4ecXFxZGdnGzsUxcisrKzw8PDA3LyKl8zfCfrM0EoOrBqvzYLk0bFyRcCUWq9GJfS4uDhsbW3x8vJCqAPyjiWlJCkpibi4OLy9vY0dTs1Xzwnu/USrHfN1H3BpDe0fg6CHwcbR2NEpBlSjulyys7NxcnJSyfwOJ4TAyclJ/VKriDZD4OWTMGgBWNjAn6/BR76w4kk48482/Z1S59WoFjqgkrkCqOOgUixtocNo7XY5QpuP9OjPELECGnhrrfa2I8HW1diRKnpSo1roiqJUk0b+MHAOvHwKhi4Gu8aw+R2Y5wc/j9TGrqtaMHVOuRK6EKK/EOKUECJaCPGfS9OEED2EEGlCiPDC21vVH6qiKBVmbg1BD8Hj62DiQej8DJzfA8sehE8CYcssSL1g7CiVanLbhC6EMAU+BwYAfsAIIYRfCYtul1K2Lby9W81xGkxsbCz+/v7lXn7MmDGsWLFCjxGVrKJxKgrOPtB3Brx0AoZ/By6+8M8H8EkA/PggXD1p7AiVKipPC70TEC2lPCOlzAV+BgbrNyylNikoUD/daxUzC/AbDKN+g+ePwF2vQtwB+LI77FygumJqsfKcFHUHiv8miwNCSlguVAhxBLgIvCKljLx1ASHEOGAcgKenZ5k7fef3SI5frN6ptvwa2/H2oDa3XS4/P5/Ro0dz+PBhWrZsyXfffcfcuXP5/fffycrKokuXLnz55Zf/OXH37rvvlrhMjx49CAkJYcuWLaSmpvL1118TFhZGQUEBU6ZM4c8//0QIwVNPPcVzzz3HwYMHeemll7h+/TrOzs4sWbIENzc3Dh48yBNPPIGNjQ3dunUr8zPExsYyatQoMjIyAPjss8/o0qULAHPmzOH777/HxMSEAQMGMHv2bKKjoxk/fjwJCQmYmpryyy+/cOHCBebOncvatWsBmDhxIsHBwYwZMwYvLy+eeOIJ/vrrLyZOnEh6ejqLFy8mNzcXHx8fvv/+e2xsbLhy5Qrjx4/nzJkzACxcuJD169fj7OzM888/D8C0adNwdXVl0qRJFfuDKlXXoKk2X2nHsbD2Rdj4JpxcC0MWglNzY0enVFB5WuglDTe4derxQ0BTKWUQ8CmwqqQNSSkXSymDpZTBLi4uFQrUkE6dOsW4ceM4evQodnZ2fPHFF0ycOJH9+/cTERFBVlZWUZIrrqxl8vPz2bdvH5988gnvvPMOAIsXL+bs2bMcPnyYo0ePMnLkSPLy8njuuedYsWJFUQKfNm0aAI8//jgLFixg9+7dt/0MDRs2ZOPGjRw6dIjly5cXJcv169ezatUq9u7dy5EjR3j11VcBGDlyJM8++yxHjhxh165duLm53XYfVlZW7Nixg4cffphhw4axf/9+jhw5QuvWrfn6668BmDRpEnfddRdHjhzh0KFDtGnThieffJKlS5cCoNPp+Pnnnxk5cuRt96foUf2G8NAPMOz/IOEkLOwKexap4Y61THla6HFAk2LPPdBa4UWklNeKPV4nhPhCCOEspUysbGDlaUnrS5MmTejatSsAjz76KAsWLMDb25s5c+aQmZlJcnIybdq0YdCgQTett2XLllKXGTZsGAAdOnQgNjYWgE2bNjF+/HjMzLQ/g6OjIxEREURERNCnTx9A685wc3MjLS2N1NRU7rrrLgBGjRrF+vXrS/0MeXl5TJw4kfDwcExNTYmKiira5+OPP46NjU3RPtPT04mPj2fo0KGAlqjL46GHHip6HBERwRtvvEFqairXr1+nX79+APz999989913AJiammJvb4+9vT1OTk4cPnyYK1eu0K5dO5ycnMq1T0WPhIDA4eAVBr9Pgg1TtPlRh3yu1XlXarzyJPT9QAshhDcQDzwMPFJ8ASFEI+CKlFIKITqhtfyTqjtYQ7m1K0UIwTPPPMOBAwdo0qQJ06dP/89FL9nZ2WUuY2lpCWhJLT8/H9CuiLx1X1JK2rRp859WeGpqaoXGZs+bNw9XV1eOHDmCTqcrStKl7bMkZmZm6Iq10G79zPXq/Ttf5pgxY1i1ahVBQUEsWbKErVu3lhnf2LFjWbJkCZcvX+aJJ54o9+dSDMDOTZsP9fAPsOE1+KKLdjI1+AlVUqCGu22Xi5QyH5gI/AmcAP4npYwUQowXQowvXOwBIKKwD30B8LAsLUvUAufPny9KqD/99FNRf7WzszPXr18vcVTLjWRX1jK36tu3L4sWLSpK8MnJyfj6+pKQkFC0/7y8PCIjI3FwcMDe3p4dO3YA8OOPP5a57bS0NNzc3DAxMeH7778vOnHZt29fvvnmGzIzM4v2aWdnh4eHB6tWrQIgJyeHzMxMmjZtyvHjx8nJySEtLY3NmzeXur/09HTc3NzIy8u7KbbevXuzcOFCQPu1ce2a9mNu6NChbNiwgf379xe15pUaRAhoPwqe2Q1NOsIfL8H3QyEtztiRKWUo1zh0KeU6KWVLKWVzKeXMwtcWSSkXFT7+TErZRkoZJKXsLKXcpc+g9a1169YsXbqUwMBAkpOTmTBhAk899RQBAQEMGTKEjh07/mcdBweH2y5zq7Fjx+Lp6UlgYCBBQUEsW7YMCwsLVqxYwZQpUwgKCqJt27bs2qV9nd9++y3PPvssoaGhWFtbl7ntZ555hqVLl9K5c2eioqKKWtP9+/fnvvvuIzg4mLZt2zJ37lwAvv/+exYsWEBgYCBdunTh8uXLNGnShOHDhxMYGMjIkSNp165dqfubMWMGISEh9OnTh1atWhW9Pn/+fLZs2UJAQAAdOnQgMlI7V25hYUHPnj0ZPnw4pqamt/2uFCNxaAKjVmkzJF3YB1+Eai332tteq9OEsRrSwcHB8sCBAze9duLECVq3bm2UeBTD0ul0tG/fnl9++YUWLVqUuIw6HmqY5LOweiKc2wEt+sGg+Vr3jGJQQoiDUsrgkt5Tl/4rBnf8+HF8fHzo3bt3qclcqYEcvWH079B/NpzdBl90hqP/U631GqTGFedSKubPP/9kypQpN73m7e3NypUrjRTR7fn5+RWNS1dqGRMT6DwBfPrAqgnw21PavKaDFmhlfBWjUgm9luvXr586qagYnrMPPLEBdn8Gf78HX98NI1eoi5GMTHW5KIpSOSam0PV5GPMHZKVqk2tc2GfsqO5oKqErilI1TTrB2E1gZQ9LB8HxNcaO6I6lErqiKFXn1Bye3AiNAuB/j8HuL4wd0R1JJXRFUapHPWdtFEzre7Up8NZPUZUbDUwl9Gq0detW7r33XmOHUe3WrFnD7NmzS33/wIEDqlKiojG3hgeXQudnYe8irbWem2nsqO4YNXeUy/qpcPlY9W6zUQAMKD0x1TQFBQV6uYqyotu97777uO+++0p9Pzg4mODgEq9zUO5EJqbQ/31w8IQNU7V+9RE/Q/2aW2G1rlAt9FvExsbSqlUrxo4di7+/PyNHjmTTpk107dqVFi1asG/fPvbt20eXLl1o164dXbp04dSpU//ZTkBAAKmpqUgpcXJyKqo4OGrUKDZt2kRsbCxhYWG0b9+e9u3bF13ev3XrVnr27MkjjzxCQEAABQUFTJ48mY4dOxIYGMiXX35Zauxbt26le/fuDB06FD8/P8aPH19UXKt+/fq89dZbhISEsHv3bn744Qc6depE27Ztefrpp4tqvWzYsIH27dsTFBRE7969AViyZAkTJ04E4JdffsHf35+goCC6d+9etN8bv0ySk5MZMmQIgYGBdO7cmaNHjwIwffp0nnjiCXr06EGzZs1YsGBBlf9WSg3XeTw89D1cidCGNSZGGzuiuk9KaZRbhw4d5K2OHz/+n9cM7ezZs9LU1FQePXpUFhQUyPbt28vHH39c6nQ6uWrVKjl48GCZlpYm8/LypJRSbty4UQ4bNkxKKeWWLVvkPffcI6WU8umnn5Zr166Vx44dk8HBwXLs2LFSSil9fHxkenq6zMjIkFlZWVJKKaOiouSN72PLli3SxsZGnjlzRkop5ZdffilnzJghpZQyOztbdujQoei9W23ZskVaWlrKmJgYmZ+fL++++275yy+/SCmlBOTy5cullNr3fO+998rc3FwppZQTJkyQS5culVevXpUeHh5F209KSpJSSvntt9/KZ599Vkoppb+/v4yLi5NSSpmSkvKfzz1x4kQ5ffp0KaWUmzdvlkFBQVJKKd9++20ZGhoqs7OzZUJCgnR0dCzaf2lqwvGgVIPz+6T8wFvK2U2lPLfb2NHUesABWUperbldLkbk7e1NQEAAAG3atKF3794IIQgICCA2Npa0tDRGjx7N6dOnEUKQl5f3n22EhYWxbds2mjZtyoQJE1i8eDHx8fE4OjpSv3590tLSSqxXDtCpUye8vb0B+Ouvvzh69GhR9ca0tDROnz5d9P6tOnXqRLNmzQAYMWIEO3bs4IEHHsDU1JT7778fgM2bN3Pw4MGiAmJZWVk0bNiQPXv20L1796JtOzo6/mf7Xbt2ZcyYMQwfPryoxntxO3bs4NdffwWgV69eJCUlkZaWBsA999yDpaUllpaWNGzYkCtXruDh4XG7P4dS2zXpqA1r/OEBWHofDPsS2gw1dlR1kkroJbhRuxzAxMSk6LmJiQn5+fm8+eab9OzZk5UrVxIbG0uPHj3+s43u3bvz+eefc/78eWbOnMnKlStZsWIFYWFhQOn1yuHmOuNSSj799NNyXw1aUi130CatuNFvLqVk9OjRzJo166Zl16xZc9ua64sWLWLv3r388ccftG3blvDw8JvelyXU9bixzeLfa/G68ModwLGZNqzx5xHwyxitDG/oRFVfvZqpPvRKSEtLw93dHdD6l0vSpEkTEhMTOX36NM2aNaNbt27MnTu3KKGXVq/8Vv369WPhwoVFvwKioqKK5gktyb59+zh79iw6nY7ly5eXOPdo7969WbFiBVevXgW0fu9z584RGhrKP//8w9mzZ4tev1VMTAwhISG8++67ODs7c+HChZve7969e1E99K1bt+Ls7IydnV2p8Sp3kHpO8NhqbYLqv96A9a+qYY3VTCX0Snj11Vd57bXX6Nq1a5kz3oeEhNCyZUtA64KJj48vSrCl1Su/1dixY/Hz86N9+/b4+/vz9NNPl9myDQ0NZerUqfj7++Pt7V00rVxxfn5+vPfee/Tt25fAwED69OnDpUuXcHFxYfHixQwbNoygoKCbppi7YfLkyQQEBODv70/37t0JCgq66f3p06dz4MABAgMDmTp1atHcoYoCaMMaH1gCXZ6DfYth+aOQW3oDRakYVQ+9Dtm6dStz584tcQLr2kgdD3Xcvv/TWunOvlq/ulvQ7ddRVD10RVFqoE5PaRUas1Lg/3rBPx9CgTqvUhXqpGgtdOzYMUaNGnXTa5aWluzdu7fEE7SKUmP59NbmLV33Cmx5D6I2wNBF4KwmPqkMldBroYCAgP+MLlGUWsvGER74BlrdA3+8DIvCoM870PEpbUINpdzUt6UoSs3gfz9M2A1e3bS+9e+HaMMblXJTCV1RlJrDzg1G/qJNQB13AL4IhfBlat7SclIJXVGUmkUI6DAGJuwEV39t7tLlj8L1hOrZfkGeNrNS5Ko6VwlS9aErilIzOXrDmLWw+3P4ewZ80VlrubeuYInq/ByIPwixO+HcDi2Z5xUmcusGEPwkdBoHtq7V/xkMTLXQq1FdrYc+ffp05s6dC8CYMWOK6sooit6ZmELXSTDuH7BrDMtHwsoJkJ1W+jq5mXDmH9jyPnx7D8xqAt8O0EbRZCRCu1FazfZRq8CzC2z/CD7xh1XPwpVIg300faixLfQP9n3AyeST1brNVo6tmNJpSrVuU5+qUg9dX7XUFcUoXP1g7GbYNge2fwxnt8GQz6FZD8i5Dhf2wrmdWis8/iDo8kCYaHMgdBwLXl3BM1QbUVNc856QFAN7voDDP0L4D9C8l1ZnpnmvWldrplwtdCFEfyHEKSFEtBBiahnLdRRCFAghHqi+EA2rttdDL++6c+bMISAggKCgIKZO1f6k//d//0fHjh0JCgri/vvvJzOzbvUvKrWcmQX0egOe/AvMreC7wbCwK8z2hB+GwY5PtETeeQI88j+YEgtPb9Mm22h1z3+T+Q1OzeGej+Cl49DrTa2V/sMwWNgFDv+gddnUFqXV1b1xA0yBGKAZYAEcAfxKWe5vYB3wwO22q+qh66ceennWXbdunQwNDZUZGRlSyn/rnicmJhZta9q0aXLBggVSSq2W+YcffiillHL06NFFNdb1rSYcD0oNlZMh5YbXpfxmoJQbp0t5eqOU2deqZ9t52VIe+kHKz0OlfNtOyjk+Um6dI2VGUvVsv4qoYj30TkC0lPIMgBDiZ2AwcPyW5Z4DfgU6VvU/GWOr7fXQb7fupk2bePzxx7GxsQH+rXseERHBG2+8QWpqKtevXy93yV5FMTgLG+g3Uz/bNrOEdiOh7SNwZgvs+kzrf9/+kfZa52fA2Uc/+66i8iR0d6B4jdQ4IKT4AkIId2Ao0Is6kNBrcz308qy7YcOGEuuejxkzhlWrVhEUFMSSJUvYunVrufapKHWSEFo/evNecOU47PkcDn8PB76Blv3AvQPYe4Cd+7/35la3364elSehl3RW4NZR/p8AU6SUBWVNkCCEGAeMA/D09CxniDVPReqh5+bm3lQP/bPPPivahoeHByYmJixduvS29dB79eqFubk5UVFRuLu7l1putzzr9u3bl3fffZdHHnkEGxsbkpOTcXR0JD09HTc3N/Ly8vjxxx+LPqOi3PFc/WDw59D7ba1KZPiPWt2ZW9Vz+TfB37jZuYN9E7B3h/qu2sgdPSlPQo8DmhR77gFcvGWZYODnwmTuDAwUQuRLKVcVX0hKuRhYDFr53ErGbHSvvvoqo0eP5uOPP6ZXr16lLhcSElKUqMPCwnjttdduqod+//3388svv9CzZ88y66HHxsbSvn17pJS4uLiwatWqcsVZ2rr9+/cnPDyc4OBgLCwsGDhwIO+//z4zZswgJCSEpk2bEhAQQHp6esW+GEWp6+o3hF7TtFteNqRf1MoTpMUX3l+Aa/GQFA1ntkLu9ZvXNzED28YQMk6rCV/NblsPXQhhBkQBvYF4YD/wiJSyxAGbQoglwFopZZmDlVU9dOV21PGg1GpSauPlr91I9oW3a/Hg0wcCH6zUZsuqh37bFrqUMl8IMRH4E20kyzdSykghxPjC9xdVKipFUZS6TAiwdtBurm0MsstyXVgkpVyHNhyx+GslJnIp5Ziqh6WUpax66Iqi3Llq3JWiUsrbzjx/p7sT6qHfritQUZT/qlG1XKysrEhKSlL/mO9wUkqSkpJuGsqpKMrt1agWuoeHB3FxcSQkVFOZTKXWsrKywsPDw9hhKEqtUqMSurm5ealXQCqKoihlq1FdLoqiKErlqYSuKIpSR6iEriiKUkfc9kpRve1YiATgXCVXdwYSqzGc6lJT44KaG5uKq2JUXBVTF+NqKqV0KekNoyX0qhBCHCjt0ldjqqlxQc2NTcVVMSquirnT4lJdLoqiKHWESuiKoih1RG1N6IuNHUApampcUHNjU3FVjIqrYu6ouGplH7qiKIryX7W1ha4oiqLcQiV0RVGUOqJGJ3QhRKwQ4pgQIlwIcaCE94UQYoEQIloIcVQI0d4AMfkWxnPjdk0I8cIty/QQQqQVW+YtPcXyjRDiqhAiothrjkKIjUKI04X3DUpZt78Q4lThdzfVAHF9KIQ4Wfh3WimEcChl3TL/5nqKbboQIr7Y32tgKesa+jtbXiymWCFEeCnr6uU7E0I0EUJsEUKcEEJECiGeL3zdqMdYGXEZ9RgrIy7DHV9Syhp7A2IB5zLeHwisR5vIujOw18DxmQKX0Qb6F3+9B9o0fPref3egPRBR7LU5wNTCx1OBD0qJOwZoBlgARwA/PcfVFzArfPxBSXGV52+up9imA6+U429t0O/slvc/At4y5HcGuAHtCx/bok1F6WfsY6yMuIx6jJURl8GOrxrdQi+HwcB3UrMHcBBCuBlw/72BGCllZa94rRIp5TYg+ZaXBwNLCx8vBYaUsGonIFpKeUZKmQv8XLie3uKSUv4lpcwvfLoHbbJxgyvlOysPg39nNwhtxpfhwE/Vtb9yxnRJSnmo8HE6cAJwx8jHWGlxGfsYK+P7Ko9q+b5qekKXwF9CiINCiHElvO8OXCj2PI7yf4HV4WFK/0cWKoQ4IoRYL4QwzISCGlcp5SXQDjCgYQnLGPt7ewLtl1VJbvc315eJhT/VvymlC8GY31kYcEVKebqU9/X+nQkhvIB2wF5q0DF2S1zFGfUYKyEugxxfNT2hd5VStgcGAM8KIbrf8n5Jc9UZZBymEMICuA/4pYS3D6F1wwQBnwKrDBFTBRjze5sG5AM/lrLI7f7m+rAQaA60BS6hdW/cymjfGTCCslvnev3OhBD1gV+BF6SU18q7WgmvVev3VVpcxj7GSojLYMdXjU7oUsqLhfdXgZVoP0uKiwOaFHvuAVw0THQMAA5JKa/c+oaU8pqU8nrh43WAuRDC2UBxXbnR7VR4f7WEZYzyvQkhRgP3AiNlYcfhrcrxN692UsorUsoCKaUO+L9S9mms78wMGAYsL20ZfX5nQghztOT0o5Tyt8KXjX6MlRKX0Y+xkuIy5PFVYxO6EKKeEML2xmO0Ex4Rtyy2BnhMaDoDaTd+ChpAqa0mIUSjwn5PhBCd0L7nJAPFtQYYXfh4NLC6hGX2Ay2EEN6FvzQeLlxPb4QQ/YEpwH1SysxSlinP31wfsRU/7zK0lH0a/DsrdDdwUkoZV9Kb+vzOCo/hr4ETUsqPi71l1GOstLiMfYyVEZfhjq/qPtNbXTe0s71HCm+RwLTC18cD4wsfC+BztLPDx4BgA8Vmg5ag7Yu9VjyuiYUxH0E7OdNFT3H8hPYTLg/tf/gnASdgM3C68N6xcNnGwLpi6w5EOwsfc+O71XNc0Wh9hOGFt0W3xlXa39wAsX1fePwcLfxH5FYTvrPC15fcOK6KLWuQ7wzohvaz/2ixv9tAYx9jZcRl1GOsjLgMdnypS/8VRVHqiBrb5aIoiqJUjEroiqIodYRK6IqiKHWESuiKoih1hEroiqIodYRK6IqiKHWESuiKoih1xP8DWO/xTqlHmpIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", + "plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", + "\n", + "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", + "\n", + "# plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", + "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------thres= 5\n", + "0.7807339449541284\n", + "0.7803983228511531\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8771796676021932 recall_benign=0.8329355608591885 recall_malware=0.9214237743451981\n", + "------thres= 6\n", + "0.7680209698558322\n", + "0.7672955974842768\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8690278393147246 recall_benign=0.8220720720720721 recall_malware=0.9159836065573771\n", + "------thres= 7\n", + "0.7522935779816514\n", + "0.75\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8469601677148847 recall_benign=0.8092243186582809 recall_malware=0.8846960167714885\n", + "------thres= 8\n", + "0.7410222804718217\n", + "0.7363731656184487\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8359253730287315 recall_benign=0.7793240556660039 recall_malware=0.892526690391459\n", + "------thres= 9\n", + "0.7229357798165138\n", + "0.7206498951781971\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8143544260617431 recall_benign=0.7523452157598499 recall_malware=0.8763636363636363\n", + "------thres= 10\n", + "0.6933158584534731\n", + "0.6918238993710691\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.814834570191713 recall_benign=0.7789115646258503 recall_malware=0.8507575757575757\n", + "------thres= 11\n", + "0.6504587155963303\n", + "0.6525157232704403\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7961147758454633 recall_benign=0.7737556561085973 recall_malware=0.8184738955823293\n", + "------thres= 12\n", + "0.5994757536041939\n", + "0.6084905660377359\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8217002376430789 recall_benign=0.7898259705488622 recall_malware=0.8535745047372955\n", + "------thres= 13\n", + "0.5566186107470511\n", + "0.5670859538784067\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8364800633747029 recall_benign=0.7857142857142857 recall_malware=0.8872458410351202\n", + "------thres= 14\n", + "0.5104849279161205\n", + "0.509958071278826\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8069013085940719 recall_benign=0.7165775401069518 recall_malware=0.8972250770811921\n", + "------thres= 15\n", + "0.4596330275229358\n", + "0.45020964360587\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.776131378517819 recall_benign=0.6663489037178265 recall_malware=0.8859138533178114\n", + "------thres= 16\n", + "0.36094364351245084\n", + "0.3438155136268344\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7450445141432245 recall_benign=0.6166134185303515 recall_malware=0.8734756097560976\n", + "------thres= 17\n", + "0.27706422018348625\n", + "0.2688679245283019\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7486449097654531 recall_benign=0.828673835125448 recall_malware=0.6686159844054581\n", + "------thres= 18\n", + "0.2182175622542595\n", + "0.2112159329140461\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7783649209005548 recall_benign=0.8172757475083057 recall_malware=0.739454094292804\n", + "------thres= 19\n", + "0.1817824377457405\n", + "0.17033542976939203\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7919276932795569 recall_benign=0.8515476942514214 recall_malware=0.7323076923076923\n", + "------thres= 20\n", + "0.15176933158584535\n", + "0.139937106918239\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8100260871351396 recall_benign=0.8634978671541743 recall_malware=0.7565543071161048\n", + "------thres= 21\n", + "0.13171690694626476\n", + "0.11740041928721175\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7995525110281643 recall_benign=0.8758907363420427 recall_malware=0.7232142857142857\n", + "------thres= 22\n", + "0.11507208387942333\n", + "0.10377358490566038\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8086390217969166 recall_benign=0.8748538011695907 recall_malware=0.7424242424242424\n", + "------thres= 23\n", + "0.0981651376146789\n", + "0.08962264150943396\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.820395115595552 recall_benign=0.8981001727115717 recall_malware=0.7426900584795322\n", + "------thres= 24\n", + "0.08256880733944955\n", + "0.07651991614255765\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8236087571719559 recall_benign=0.9074914869466515 recall_malware=0.7397260273972602\n", + "------thres= 25\n", + "0.06828309305373526\n", + "0.0660377358490566\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8465207631874299 recall_benign=0.9152637485970819 recall_malware=0.7777777777777778\n" + ] + } + ], + "source": [ + "import collections\n", + "pd_metric=collections.defaultdict(list)\n", + "for thres in list(range(5,26,1)):\n", + " print('------thres=',thres)\n", + " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train.drop('proportion',axis=1),X_test.drop('proportion',axis=1),thres,['malwareNum','label'] ,2)\n", " pd_metric['thres'].append(thres)\n", " pd_metric['balanced_accuracy'].append(score)\n", " pd_metric['benign_recall'].append(recall1)\n", @@ -2992,7 +3396,25 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(pd_metric).to_csv('../../xgboost_threshold_count.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.DataFrame(pd_metric)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -3016,290 +3438,85 @@ " \n", " \n", " \n", - " thres\n", - " balanced_accuracy\n", - " benign_recall\n", - " malware_recall\n", - " benign_precision\n", - " malware_precision\n", + " malwareNum\n", " \n", " \n", " \n", " \n", - " 0\n", - " 0.01\n", - " 0.854852\n", - " 0.803783\n", - " 0.905921\n", - " 0.666667\n", - " 0.951744\n", - " \n", - " \n", - " 1\n", - " 0.03\n", - " 0.859124\n", - " 0.810934\n", - " 0.907314\n", - " 0.681992\n", - " 0.951405\n", - " \n", - " \n", - " 2\n", - " 0.05\n", - " 0.859041\n", - " 0.811287\n", - " 0.906795\n", - " 0.747967\n", - " 0.933746\n", - " \n", - " \n", - " 3\n", - " 0.07\n", - " 0.861583\n", - " 0.816000\n", - " 0.907165\n", - " 0.773900\n", - " 0.926798\n", - " \n", - " \n", - " 4\n", - " 0.09\n", - " 0.859165\n", - " 0.806697\n", - " 0.911634\n", - " 0.792227\n", - " 0.918642\n", - " \n", - " \n", - " 5\n", - " 0.11\n", - " 0.856246\n", - " 0.802343\n", - " 0.910149\n", - " 0.797671\n", - " 0.912508\n", - " \n", - " \n", - " 6\n", - " 0.13\n", - " 0.854162\n", - " 0.793584\n", - " 0.914739\n", - " 0.815186\n", - " 0.903394\n", - " \n", - " \n", - " 7\n", - " 0.15\n", - " 0.854121\n", - " 0.779817\n", - " 0.928425\n", - " 0.850000\n", - " 0.890196\n", - " \n", - " \n", - " 8\n", - " 0.17\n", - " 0.823251\n", - " 0.744875\n", - " 0.901627\n", - " 0.831004\n", - " 0.844768\n", - " \n", - " \n", - " 9\n", - " 0.19\n", - " 0.843597\n", - " 0.820850\n", - " 0.866345\n", - " 0.830092\n", - " 0.858739\n", - " \n", - " \n", - " 10\n", - " 0.21\n", - " 0.845145\n", - " 0.801878\n", - " 0.888412\n", - " 0.867886\n", - " 0.830658\n", + " count\n", + " 9538.000000\n", " \n", " \n", - " 11\n", - " 0.23\n", - " 0.842389\n", - " 0.775773\n", - " 0.909006\n", - " 0.903000\n", - " 0.787805\n", + " mean\n", + " 12.433424\n", " \n", " \n", - " 12\n", - " 0.25\n", - " 0.825224\n", - " 0.731474\n", - " 0.918974\n", - " 0.920762\n", - " 0.726683\n", + " std\n", + " 8.518853\n", " \n", " \n", - " 13\n", - " 0.27\n", - " 0.792966\n", - " 0.656809\n", - " 0.929124\n", - " 0.945545\n", - " 0.590984\n", + " min\n", + " 0.000000\n", " \n", " \n", - " 14\n", - " 0.29\n", - " 0.780516\n", - " 0.822699\n", - " 0.738333\n", - " 0.895194\n", - " 0.605191\n", + " 25%\n", + " 7.000000\n", " \n", " \n", - " 15\n", - " 0.31\n", - " 0.814713\n", - " 0.829871\n", - " 0.799555\n", - " 0.942602\n", - " 0.542296\n", + " 50%\n", + " 14.000000\n", " \n", " \n", - " 16\n", - " 0.33\n", - " 0.828884\n", - " 0.866559\n", - " 0.791209\n", - " 0.955109\n", - " 0.536313\n", + " 75%\n", + " 17.000000\n", " \n", " \n", - " 17\n", - " 0.35\n", - " 0.839876\n", - " 0.873832\n", - " 0.805921\n", - " 0.966131\n", - " 0.502049\n", - " \n", - " \n", - " 18\n", - " 0.37\n", - " 0.845233\n", - " 0.881143\n", - " 0.809322\n", - " 0.975028\n", - " 0.446262\n", - " \n", - " \n", - " 19\n", - " 0.39\n", - " 0.847486\n", - " 0.890740\n", - " 0.804233\n", - " 0.980054\n", - " 0.405333\n", + " max\n", + " 43.000000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " thres balanced_accuracy benign_recall malware_recall benign_precision \\\n", - "0 0.01 0.854852 0.803783 0.905921 0.666667 \n", - "1 0.03 0.859124 0.810934 0.907314 0.681992 \n", - "2 0.05 0.859041 0.811287 0.906795 0.747967 \n", - "3 0.07 0.861583 0.816000 0.907165 0.773900 \n", - "4 0.09 0.859165 0.806697 0.911634 0.792227 \n", - "5 0.11 0.856246 0.802343 0.910149 0.797671 \n", - "6 0.13 0.854162 0.793584 0.914739 0.815186 \n", - "7 0.15 0.854121 0.779817 0.928425 0.850000 \n", - "8 0.17 0.823251 0.744875 0.901627 0.831004 \n", - "9 0.19 0.843597 0.820850 0.866345 0.830092 \n", - "10 0.21 0.845145 0.801878 0.888412 0.867886 \n", - "11 0.23 0.842389 0.775773 0.909006 0.903000 \n", - "12 0.25 0.825224 0.731474 0.918974 0.920762 \n", - "13 0.27 0.792966 0.656809 0.929124 0.945545 \n", - "14 0.29 0.780516 0.822699 0.738333 0.895194 \n", - "15 0.31 0.814713 0.829871 0.799555 0.942602 \n", - "16 0.33 0.828884 0.866559 0.791209 0.955109 \n", - "17 0.35 0.839876 0.873832 0.805921 0.966131 \n", - "18 0.37 0.845233 0.881143 0.809322 0.975028 \n", - "19 0.39 0.847486 0.890740 0.804233 0.980054 \n", - "\n", - " malware_precision \n", - "0 0.951744 \n", - "1 0.951405 \n", - "2 0.933746 \n", - "3 0.926798 \n", - "4 0.918642 \n", - "5 0.912508 \n", - "6 0.903394 \n", - "7 0.890196 \n", - "8 0.844768 \n", - "9 0.858739 \n", - "10 0.830658 \n", - "11 0.787805 \n", - "12 0.726683 \n", - "13 0.590984 \n", - "14 0.605191 \n", - "15 0.542296 \n", - "16 0.536313 \n", - "17 0.502049 \n", - "18 0.446262 \n", - "19 0.405333 " + " malwareNum\n", + "count 9538.000000\n", + "mean 12.433424\n", + "std 8.518853\n", + "min 0.000000\n", + "25% 7.000000\n", + "50% 14.000000\n", + "75% 17.000000\n", + "max 43.000000" ] }, - "execution_count": 88, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.DataFrame(pd_metric)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(pd_metric).to_csv('../../xgboost_threshold.csv',index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "df=pd.DataFrame(pd_metric)" + "X[['malwareNum']].describe()" ] }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 96, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3321,22 +3538,22 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 95, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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6+lfmH5pPZl4mj7R+hAlBEwxXBKxQZl4m03ZMY9P5TfTz6se7Xd7FxsRCq/q45wu4sBcs7aDdKAh5GhrosQKlgX2w7wNWRK1g9yO7MTOpXaOplJpPdblUUIFOsjo8nvmbT3MuKZOgJg683KclYS2ca01iT8lOYcHhBfwa9StO1k7M6DqDbu7dDLLvuPQ4Jm2ZRExqDC91eInH/B777/cWd1BL7JErAQmt7tVqtnt2rrWlfW/448wfTN0+lRWDVuDr6GvscJQ6RiX0Ssor0PHrwTgWbD7NxbRsOnk58mhoU0KbOeFiWzsugIlIjODNnW8SnRrNUwFP8UzbZ/Taatx3aR8v//MyBbKAud3n0sW9S9krpMXDvsVwcAlkp0LjdtD5WWgzpNZOrXfu2jnuXXkv00Onc3/L+40djlKDSCnZdjoRT0cbvJ0rVw9IJfQqyskvYPn+C3z2dzRX03MAaOlany7NnQlt7kRnbyfsbWpu8snKz2L2vtn8dvo3Orh2YE73OWXWdq8MKSXLTy1n9r7ZNLVryoJeCyo2kUduBhz5SetbT4rWrky9awoEP16tcRqClJKuP3elb9O+TO8y3djhKDXE4fMpfLDhJHvOJPNoZ0/eGxJQqe2ohF5N8gt0RFy8xu6YJHbFJLI/NpnsPB1CQJvGdkUJvqOXI/Uta17f6e8xvzNjzwyszayZ1W3W7VvP5ZRXkMf7+95nRdQKunt054OwDyrfZ6/TQfRGrfzAhb0w7h/tqtRa5umNT5Ocncwvg34xdiiKkUVfTefDP0/xZ+QVnOpZ8FwvHx4JaVrpa19UQteT3Hwd4RdSixL84fOp5BboMDMRBDVxILSZE12aO9G+aYMac0XqmdQzvPzPy8SkxvBU4FNMCJpQpS6YpKwkXtr6EoeuHuJJ/yd5rt1z1TO+PDsN5rcF1zYw+vda16++4NACvon4ht2P7FbFuu5QF1OzmL/pNL8cvIC1uSnjujfnyTDvKjf2VEI3kKzcAg6eS2FXTCK7zyRxNC6NAp3EwsyEDp4N6OPnyohOnlhbGDe5Z+VnMWvvLFZGryTYNZg53efgYuNS4e2cTD7JpL8nkZydzLtd3mVgs4FVjk2nk+yPTeb3oxfpeGUFgy99AiN+Bt/adZHOlvNbmLRlEt8N+I52DdsZOxzFgFIycln4TwxLdsWChEc7N+XZns1xql89591UQjeS9Ow89scmsys6iZ0xSZy4dK2ojszIkKZGT+xrYtbw3p73tC6YsFl0aVz+Lpg/Y//kjR1vYG9pz/xe82njVPl6LFJKjsalsebIRf44eonL17KxMjfBROazzmwyzvWtqP/i/lp1kjQhM4Fev/Ti1Y6vMspvlLHDUQwgMzefb3fGsuifGK7n5DOsnQcv3N2CJo7Ve+W2Sug1xL6zyczfHMXO6CSc61sy/q5mRi8QFpMaw8tbX+ZM2hnGBY5jQtCEMrtMdFLH5+Gfs/joYtq6tGVez3k4WztXeL9SSk5dSef3Ixf5/cglzidnYm4quKtlQwYFuXF3a1cupmax/IdFvJH+HitcX6DPmDewt649Sf3uX+6mvWt75nSfY+xQFD3KK9Dx8/4LLNh8moT0HO5u7crkfr74NrLVy/5UQq9h9p5JYv5mrUCYi61lYYvd02iJPTMvk/f3vs/qmNV0atSJ2WGzS+yCycjL4LXtr7HlwhaG+gzljc5vYGFqUaF9nU3MKEziFzl99TqmJoIuzZ0YFNSYfn6N/jNaKC+/gCsLemOdFs3Dll8wfXgXuvpU/D8QY3hxy4ucSjnFumHrjB2Kogc6nWTtsUt89NcpziVl0tGrAVP6tyLYy1Gv+1UJvYbacyaJ+ZtOs/tMEg1tLZnQozkjOhkvsa+KXsXMPTOpZ16P2d1n09mtc9F7F65dYNKWSZxNO8vkjpN5pNUj5b7IKj41iz+Oai3xY/FpAHTydmRQUGMG+DfC+XZ9i/GH4P96ssz8fl5Pv58xXbyYOqBVjTnRXJqvj33NJ4c+YcfDO7C3tDd2OEoVSSlJz8knNSOPqCvpzNsUReTFa7RqZMur/X3p6WuYK8pVQq/hdsck8cmmKPaeTaahrSXP9GjOw0ZK7NEp0bzyzyucSTvDhKAJjAscx/4r+3nln1eQUjL3rrnlmu9Tp5P8djien/ed58C5FACCmjgwKNCNewLdcLOv4MiPX59CHl/NJ61/Yv6BbJq71OPj4W0JauJQiU9pGHsv7WXsX2NZdPciurp3NXY4SjFSSlIy80jOyCElM4+UjFxSM/NIzswlJTOX1Iw87T5Tu0/JzCM1M5f8YvWdPBpY81Kflgxu627QYn4qodcSu2IS+WTjafbFJuNqZ8kzPXx4qGMTgyf2zLxMZu6dyZqYNbR2bE1UShRedl4s6LWgXFPfHbmQylurIzgSl0ZL1/oMbuvOoMDGeDpV4eRQ6gX4tAP4DWZH4CwmrzjC1fQcJvb0YWIvH8xrYNnj9Nx0uv7UlWfaPsP4oPHGDueOl5aVx+6YRLadTmT76QQuJGeVuJy5qaCBjQUNbCxwsDHXHtczx8HGggaFz51tLenS3AlLM8M3ulRCr0WklOyOSWLepij2x6bQyM6KZ3o256GOTUo8eKSU6KRWf0YnZdG9TgcFhc/NTQUONhXr64Z/u2A6u3VmVtis214slJyRy5wNJ1l+4ALO9S2ZNrA1g9s2rr6foZumw4558NQW0hwDmL4mkpWH4wn0sOfj4W3xaWjYAmTlMXjVYDxtPfm096fGDuWOk1+g40hcKtuitAQefiEVnYR6FqaENnemczNHXGwtb07e9SyoZ2Fao2s2qYReC0kp2RWTxLyN2iQcVuYmmJmYUKCTFEiJLEre5dteVx8nHu/iTa9WDTGpwM/DrPwsrEytyjzAC3SSZXvPMfevKDJy8nm8qxeTerfA1qqaR6RkX4MF7cClFYxZC0Kw7tglpq08RmZuAVP6t2JMF68KfT59m7ZjGjvjd7Jl+JYanSTqivNJmWw7ncD20wnsikkiPTsfISDQw4HuLZwJa+FCO0+HGvmLrrzKSug17/p0BdBqtXf1caZLcyd2Riex+eQVBAITAaYmAhMTgan4997UhGKPBSY37k0ESddzWL7/AmO/O4CXkw2ju3jxYHCTcl2xdrurHA+eS+bNVZEcv3SNLs2deOe+NrRw1c9wLazsoMdUWPcKnFoHre5hYIAbwU0bMPW3Y7y79jibTlzhwweDcHeoGVdn+jv7syZmDZczLuNW383Y4VS77LwCtp5KoKGdJQHu9gZPlOnZeeyOSWL76US2nU4omhy+sb0V9wS4EdbCha4+TpX6hVobqRb6HSKvQMefkZf5ZsdZDp1PxdbSjOEdmzA61KtSfdsJ6TnMXn+SXw/F4WZvxRv3+DEwoJH+W6EFebCwC0gdPLOn6GIjKSXL919gxtrjmAjB9PvaMKy9u9FbxRGJEYz4YwQf9/iYPk37GDWW6nQhOZMf9pxj+YELpGZq88lam5vSoWkDOnk70snbkbZNHKr1/I9OJzmTmMGRC6kciUvlyIVUIi5eo0AnsbEwJbSZE2EtnAlr6UIz53pG/9vrS5W7XIQQ/YH5gCnwlZRy9i3v2wM/AJ5orf65Uspvy9qmSujGE34hlW93nuWPo5cokJI+rV15ops3Id6Ot/1HkF+g47vd55i3MYrs/AKeCmvGsz19qGfIYmSn1sNPD8OADyFk3E1vnU/K5OVfwtkfm8I9gW68PzTAqBcj5RbkErIshFF+o3ipw0tGi6M66HSSHdGJfLc7ls0nr2IiBH0Ly1lk5OSz92wye88mc/LyNaQEC1MT2jZxIKSZluDbezao0HFy9Vo24YXJO/xCKkcvpJGekw9o/eABHva092xAWAsX2jd1MMoJSmOoUkIXQpgCUUAfIA7YD4yQUh4vtszrgL2UcooQwgU4BTSSUuaWtl2V0I3vclo2P+w5x497z5GSmUdrNzse7+rFfUGNS2xZ7TmTxNurIzl1JZ3uLV2YPsiPZi5GOBEpJSwdBFci4flwsLp5jHeBTrLonxjmbYzC1c6KeQ+1pZO3fi/2KMuItSOwNrfmm37fGC2GqriWnceKA3F8v+ccZxMzcK5vwYhOnjwS4lni8NO0TK3kxd6zSew7m1zUijYzEfi72xNS2IIP9nIs+s/2ek4+x+LStOR9Xkvil9KyAa2LsVUjW4KaONC28NbcpX6tmve3OlU1oYcC06WU/QqfvwYgpZxVbJnXgCbAs4AXsBFoKaXUlbZdldBrjuy8AlYdjufbnbGcupKOUz0LRnZuyqOdPWloa8WVa9nM/OMEa45cxN3BmrcG+dHXz9W4P2kvhsPiHtB1EvR5t8RFwi+k8vzPh7mQnMnEnj5M6t0CMyOcDJu5Zya/n/mdnQ/vrJ5KlAZy6nI63+2OZeXheDJzC2jn6cDoUC8GBDSqUGv4ek4+B8+lsO9sEnvPJHMkLpW8AokQ0LqRHfk6HaevXudGKmrqZEOQh0NhArenTWP7Gn8RmSFVNaE/APSXUo4tfD4KCJFSTiy2jC2wBmgF2AIPSSn/KGFb44BxAJ6enh3OnTtXuU+k6MWNkTXf7jzL5pNXMTPRJtHeFZ1Ink4y/q7mTLirudGLihVZOR4ifoOJ+0udk/R6Tj5vr47k10NxtPN0YP5D7ao2Hr4SVkev5o2db7Bq8CqaOzQ36L4rKr9Ax8bjV1i6O5Y9Z5KxMDPhvqDGPBbalEAPh2rZR3ZeAYfOp7DvbDL7Y5MxL+yaCWriQJCHA4717owTmJVV1YT+INDvloTeSUr5XLFlHgC6Ai8BzdFa6EFSymulbVe10Gu2s4kZLN0Vy+rweDo0bcCb9/rR1KlyU2bpTVqcdrFR60Fw/1dlLvr7kYu8vvIYUsKMIW0Y2s7DQEFqNegHrx7MjK4zGOIzxGD7rYiE9Bx+3neeZfvOcyktG3cHa0aFNmV4cBOVYGuYqg5bjEPrTrnBA7h4yzKPA7Ol9r9DtBDiLFprfV8l4lVqAG/neky/rw3T76t8WVy9s/eA0ImwfS6ETACPDqUuOiioMe08HXhxeTgvLj/C1lMJzBjij111j5UvgZe9F/XM6xGRGFGjEnpCeg4bj1/hz8jL7IpJJK9AEtbCmXcH+9OrVcM7to+6NitPQt8PtBBCeAPxwMPAI7cscx7oDWwXQrgCvsCZ6gxUUUrU7QU4tBT+egMeX1fmzEYeDWz46anOfLE1hvmbT3PwXArzH25Lh6b6PWFqIkxo49SGiMQIve6nPM4lZfBn5GX+jLzCofMpSAmejjY83tWb4cFNauTVtkr53TahSynzhRATgT/Rhi1+I6WMFEKML3x/ETADWCKEOAYIYIqUMlGPcSuKxtIWer4Oa1+Ek2u17pcymJmaMKl3C7r6OPPC8sMM/3IPz/XyYWJPH72eMPV39ue749+RW5Bb4ZLDVSGl5Pila/wZeYW/Ii9z8nI6AH5udrzQuyX9/F3xdbWts2O27zTqwiKl9ivI1y420uXBM3vBrHwJMz07j7dWa/Vggps2YN5Dbat9dpkbNp3bxItbX2TZwGUEuFRutvfyKtBJDsQma0n8+GXiUrIwERDs5UhfP1f6tWmkt8+p6J+69F+p20zNoO8MWDYcDn4LIU+XazVbK3PmPdSWu1q68MaqCAbO3857Q/0Z3Na92kP0d/YH4FjiMb0k9LwCHdtPJ/BnxBU2nbhCUkYuFqYmdGvhzHO9fOjd2vX2decVg0jNTsXO0g4TUf2/CFVCV+qGFn3B+y7YOhsCHwJrh3KvOqSdO+09G/DC8sM8/3M4/5xK4N0h/lWenb04VxtXnK2d9dKPfvpKOi/+L5yI+GvYWprRs1VD+rVpxF2+LtX6GZSqO5N6hvGbxjOo+SCea/fc7VeoIPXXVuoGIaDve/Bld9j+kdZirwBPJxv+93QoC/6O5rO/T3PwfAoLHm5XbRNoCCHwd/YnIqn6ErpOJ/l2VywfbDhJfUsz5j/clv7+FbvoRzGcQ1cO8dzfz2FuYk4vz1562UftrSGpKLdyC4S2j8DeRZASW+HVzUxNeKlPS34eF0pevo77F+5i4dYYdOWtUXwb/k7+nE07S3puepW3FZ+axaNf72XG2uOE+Tiz4YUwBrd1V8m8htp4biNP/fUUjlaO/DDwB9o46Wc4sEroSt3S6w0QprC55HIA5dHJ25H1z3enj58rH2w4yWPf7OPqtewqhxbgrPWdRyZFVnobUkpWHo6j/yfbCL+QyuxhAXw1OpiGtlZVjk/Rjx9P/MjLW1+mtVNrvhvwHR62+ruoTSV0pW6xawxdnoOIXyH+YKU3Y29jzhcj2zNrWAAHziXTf/52Np+4UqXQ2jhrrbLK9qOnZOTy7LJDvLj8CL6utqx/PoyHO3mqIYc1lE7q+OjAR8zeN5ueTXryVd+vaGDVQK/7VAldqXu6PAfWjvD3e1XajBCCEZ08WftcN1ztrHhy6QGmr4kkO6+gUtuzt7TH09azUgl9y6mr9P1kGxuPX+HV/r4sfzq05pViUIrkFuQyddtUlkQu4WHfh/m4x8dYmen/V5RK6ErdY2UHYS9BzN8Qu6PKm/NpaMvKZ7owposXS3bFMuTznURfrVw/uL+zP8cSj5V7+YycfF5feYzHv92Po40Fq57tyjM9fNRl+TXYtdxrjN80nvWx63mh/Qu8HvK6wapsqoSu1E0dx4KtG2yeAdVw8ZyVuSnT72vDN2OCuZqew72f7uCnfeep6IV5Ac4BXM28ytXMq7dd9uC5FAYu2M5P+84zrnszVk/sSpvG9rddz5ByC3JZe2YtVzKq1h1VV1zOuMzo9aM5fPUws8Jm8WTAkwbtElMJXambzK3hrlfhwh44vbHaNturlSsbng8juKkjr/12jGd+PERa4RRs5XHjAqOyul1y83XM/fMUDy7aRX6B5KenOvP6wNY1qia4lJK/Yv/ivlX38dr21xi+djh7L+01dlhGFZUSxch1I7mccZmFdy/k3mb3GjwGldCVuqvdKGjgBX+/C7pS51qpsIZ2Vnz3RCemDmjFxuNXGDB/G/vOJpdr3VaOrTAVpqUm9NNX0hn6xU4+2xLNsPYebHghjM7NnKot9uoQkRjBmA1jePmfl7E2s+b9bu/jYOnAuI3j+OrYVxX+1VIX7Lu0j9HrR4OEJf2X0Nmts1HiUBcWKXWXqTn0eB1WjoPjq8B/WLVt2sREMP6u5oQ2c2LSz4d5ePFunuvVgud6lV3kywQLvO182BMfTjvbBJKu55KUkUtyRg5XruWw5shF6luasejRDvT3b1Rt8VaHyxmXWXBoAb+f+R1HK0feCn2LYT7DMDUxpbdnb97e9TbzD83naMJRZnabia2FrbFDNoh1Z9Yxbec0mto2ZeHdC3Gr72a0WFRxLqVu0xXAwq7/Fu4yrf42zPWcfN5aHcFvh7QiXwMD3EjOyCUpI6dYws4l6XoO17LzsWz0G+Z2R7ke9RY3fiSbmQga1LOgk7cj0we1wcW25tRdyczL5NvIb1kSsQSd1DHKbxRjA8ZS3+LmUrtSSn488SMfHfiIxvUb83GPj/F19DVS1PonpWRJ5BI+PvgxHVw7ML/nfOwt9X+Oo0ozFumLSuiKwZxYC8tHwuDPod2jetvN6vB4pq2M4HpOPiYCHOtZ4lTPAqf6FjjWs8C5viWO9SyIy9/K+ssLmBn8A20aNsepngV2VuaY1LCRKzqpY03MGhYcWkBCVgL9vfrzQocXcK9fdvGyw1cP88rWV7iWe423Qt9iUPOySxpXhpSS8IRwjiYcRUqJRKKTOiQSKW9+fOM9XeEUxzfeMxEm2FrYYm9hj72lPXYWdthb/vvY2sy61BOaBboC5uyfw7KTy+jn1Y+Z3WZiaWqY/4RVQlfubFLC//WCjAR47iCY6e8fXkZOPrn5OuytS0/QUSlR3L/mfmaFzaq2E2cp2SnsvbQXTztPvO29sTazrtL29l/ez4f7P+RE8gkCnQOZ3HEybRu2Lff6iVmJvLrtVfZf3s9Dvg/xasdXq6UOfL4un03nNrE0cmmF6uIIBEIITDABASaYoJM68mV+qeuYmZiVmuzPpp1l58WdjPIbxSvBr+ilcmKpn0WVz1XuaEJA77fg+yFwcEm5y+tWRj1LM+rd5v+LZvbNsDazJiIxoloSupSS13e8zo54bcy9QOBe3x0fBx+aOTTDx8GH5g7Ny5Xoz187z8cHP2bz+c00qteID8I+oL93/wonLGdrZxb3WcyCQwv4NvJbjicd56O7Pqp0/3J6bjq/nf6NH0/8yKWMSzS1a8obIW/Q16svFqYW/yZsYXJT0hZCFL13KyklWflZXMu9RlpOWtH9TY9z07iWc4203DSuZl4lOjWatJw08nR5TA6ezGNtHqvU59EXldCVO0OzHuAVBts+1LpdLIx3laWZiRmtHVtX6AKjsvx9/m92xO9gXOA4fBv4EpMWQ0yqdttxcQf5Oq0VeiPRN3doTnOH5kUJv5l9M3ILcvny6Jf8dPInLEwsmNRuEqP8RlXp6kYzEzNeCn6JQJdA3tj5BsPXDueD7h/QpXGXcm/j4vWL/HjiR349/SsZeRkEuwbzWqfXuKvJXVVuFQshsDG3wcbchkb1KnYCWkpZI0suqISu3BmEgF5vwjd9tWqMYS8bNZwA5wB+OvkTebo8zE0qP1F1Zl4ms/fPpkWDFkwImoCZyc3/pPN0eVy4doGYtBiiU6OLEv3OiztvSvQWphbkFuQyrMUwJrabiLO1c5U+X3F3N70bHwcfXtz6IuM3jmdiu4mMDRhbZkI+lnCMpceXsuncJgD6efXjMb/HiurhGFtNTOagErpyJ/EMgZb9Yed8CH6yQpNgVDd/Z39ydbmcTjmNn5Nfpbez+OhiLmdc5oOwD/6TzAHMTcy1VrhDM/o07VP0ep4ujwvpF4hJ1RJ9UlYSD7Z8UG+jUrzsvfhx4I+8s/sdPj38adHQxuKjQgp0BWy9sJXvjn/HoauHqG9en1F+oxjZemSFW9B3KpXQlTtLrzdgUTfY9Sn0ftNoYRS/YrSyCf1M6hmWRi5lcPPBtHdtX6F1zU3MaWavdbcUT/T6ZGNuw+yw2bRt2JY5++fw8NqHmddzHp62nqyKXsUPJ37gQvoF3Ou7M6XjFIa2GEo9c1WArCJUQlfuLI0CwP9+2LNQOzlav6FRwnCv704DywZEJEYw3Hd4hdeXUjJz70ysza15scOLeohQP4QQjGg1Aj8nP17a+hKPrnsUC1ML0nPTCXQJ5IX2L9DLs1eJvzaU21PfmnLn6fE6RK6C7R/DgNlGCUEIQRvnNpU+Mbr+7Hr2Xd7HGyFv4GRds0oDlEeQSxD/u/d/fLD/A3RSx6OtH63QsEilZCqhK3ceZx9tqroDX0Pos+DQxChhBDgHsOviLjLzMrExtyn3etdzrzP3wFzaOLXhgZYP6DFC/XKydmJO9znGDqNOUcW5lDvTXVO0+38+MFoI/s7+6KSO40nHK7Te5+Gfk5iVyJud3zRYnW2ldlAJXbkzOTTRRrqEL4PEaKOEUJ5Surc6lXyKZSeX8WDLB2vMED6l5lAJXblzhb0EZlaw9X2j7N7RyhH3+u7l7kfXSR3v7XkPewt7JrWfpOfolNqoXAldCNFfCHFKCBEthJhawvuThRDhhbcIIUSBEMKx+sNVlGpUvyF0Hq9NKH25eq7arCh/Z38ikyLLtezq6NWEJ4TzYocXDVLVT6l9bpvQhRCmwOfAAMAPGCGEuGngrJTyQyllWyllW+A14B8pZfkq/iuKMXV5DqzsqzyhdGX5O/kTfz2epKykMpdLy0lj3sF5tGvYjsE+gw0UnVLblKeF3gmIllKekVLmAj8DZR1RI4CfqiM4RdE76wbQ9XmI2gDnDT+F2o1+9Nu10ucfms+13GtMC5lm0Mp+Su1SniPDHbhQ7Hlc4Wv/IYSwAfoDv5by/jghxAEhxIGEhISKxqoo+hEyHuq5wN/VM6F0Rfg5+WEiTMo8MRqRGMGKqBWMaDWiTk8YoVRdeRJ6SVVoSjvqBwE7S+tukVIullIGSymDXVxcyhujouiXRT3oPhlit8OZrQbdtY25Dc3sm5V6YrRAV8CMPTNwtnbm2bbPGjQ2pfYpT0KPA4pfeeEBXCxl2YdR3S1KbdRhDNg3gc3vGryVHuAcQERiRImTK6+IWsHxpONM7jj5P1O+KcqtypPQ9wMthBDeQggLtKS95taFhBD2wF3A6uoNUVEMwMxSu9jo4iE4+YdBd+3v7E9qTirx1+Nvej0pK4n5h+cT0iiE/l79DRqTUjvdNqFLKfOBicCfwAngf1LKSCHEeCHE+GKLDgX+klJm6CdURdGzoBHg5KONeNEVGGy3pV1g9PHBj8nKz+L1zq/X2PrbSs1SrtPlUsp1UsqWUsrmUsqZha8tklIuKrbMEinlw/oKVFH0ztQMek6DhBNwap3BdtuiQQssTCxu6kc/dOUQa2LWMNpvNM3smxksFqV2U+OfFKW41veBtSOc+N1guzQ3Mae1U+uiFnq+Lp/39r6HWz03xgWOM1gcSu2nErqiFGdqBi37QdSfUFD6jPDVzd/ZnxPJJ8jX5bPsxDJOp5xmSqcpFarCqCgqoSvKrXwHQHYqXNhjsF36O/uTlZ/Fnkt7+Dz8c8Lcw+jVpJfB9q/UDSqhK8qtmvcCUws4td5guwxwDgDg9e2vk6/L57VOr6kToUqFqYSuKLeytAXvu7ThiwYak+5p64mthS0pOSmMDRhLEzvjTLqh1G4qoStKSXwHQMpZSIwyyO6EEHRw7UBTu6Y8EfCEQfap1D0qoStKSVoWXshjwOGLs8Nms+yeZViaWhpsn0rdohK6opTE3h3c2hq0H72eeT3sLOwMtj+l7lEJXVFK4zsQLuyD66oyqFI7qISuKKXxHQBIOP2nsSNRlHJRCV1RStMoAOw84KTh+tEVpSpUQleU0gihtdJj/oa8LGNHoyi3pRK6opTFdwDkZ8GZf4wdiaLclkroilIWr25gYWvQ4YuKUlkqoStKWcwswae3Nom0TmfsaBSlTCqhK8rttLoHrl+Bi4eNHYmilEkldEW5HZ+7QZiqbhelxlMJXVFux8YRmnYx6FWjilIZKqErSnn4DoCrkZASa+xIFKVUKqErSnn4DtDuT20wbhyKUgaV0BWlPBybgUsr1Y+u1GgqoStKefkOgHM7ISvV2JEoSolUQleU8vIdCLp8iN5k7EgUpUQqoStKebl3gHouqttFqbFUQleU8jIxhZb94PQmyM81djSK8h8qoStKRfjeAzlpcH6XsSNRlP9QCV1RKqJZDzCzUhcZKTVSuRK6EKK/EOKUECJaCDG1lGV6CCHChRCRQghVa1SpmyxsoFlPrR9dSmNHoyg3uW1CF0KYAp8DAwA/YIQQwu+WZRyAL4D7pJRtgAerP1RFqSF8B0Dqebh63NiRKMpNytNC7wRESynPSClzgZ+Bwbcs8wjwm5TyPICU8mr1hqkoNUjL/tq9Gu2i1DDlSejuwIViz+MKXyuuJdBACLFVCHFQCPFYSRsSQowTQhwQQhxISFAzqSu1lK0ruAerfnSlxilPQhclvHZr56EZ0AG4B+gHvCmEaPmflaRcLKUMllIGu7i4VDhYRakxfAdA/EFIv2zsSBSlSHkSehzQpNhzD+BiCctskFJmSCkTgW1AUPWEqCg1kO9A7b46W+l5WZB9rfq2p9xxypPQ9wMthBDeQggL4GFgzS3LrAbChBBmQggbIAQ4Ub2hKkoN0rA1ODStvoSefhm+CIX/6wl52dWzTeWOc9uELqXMByYCf6Il6f9JKSOFEOOFEOMLlzkBbACOAvuAr6SUEfoLW1GMTAitlX5mK+RmVG1bGUnw3WAtqSdFw/aPqiVE5c5TrnHoUsp1UsqWUsrmUsqZha8tklIuKrbMh1JKPymlv5TyEz3Fqyg1h+8AKMiBmC2V30Z2GvwwVJs4Y+QvEDAcdsyDhKhqC1O5c6grRRWlspp2ASv7yne75GbAj8PhynEY/j14h0G/mdrFS3+8pC5cUipMJXRFqSxTc2jRF6I2gK6gYuvmZcPPj0DcPrj/K2jZV3u9fkO4ezrEbocjP1d7yErdphK6olSF7wDITIS4A+VfpyAPfhmj9b8P/hzaDLn5/fZjwKMT/DUNMpOrL1alzlMJXVGqwuduMDEr/1WjugJY+TRErYeBc6HtI/9dxsQE7p2nzYy06e1qDVep21RCV5SqsLIHr27l60fX6eD35yHiV61bpdNTpS/byB9Cn4VD38G53dUWrlK3qYSuKFXlOxAST0FSTOnLSAl/vg6Hv4ewV6Dbi7ffbo+pYN8E1r6oJtRQykUldEWpqqJiXWW00rfMhL0LIWQC9HqjfNu1qAcDP4SEE7D7s6rHqdR5KqErSlU1aAqu/qX3o++YB9s+hHajoP8s7aKk8vIdAK3uhX/maGPVFaUMKqErSnXwHQDnd/93VMq+/4NN08H/fhg0v2LJ/IYBH2jzma6brMamK2VSCV1RqoPvAJA6OP3Xv6+FL4N1r0DLATD0Sy0pV4a9B/R8Xdv28dXVE69SJ6mErijVwa0d2Lr92+0SuQpWPwved8GDS7SLkKqi09PQKAA2TFUVGZVSqYSuKNXBxEQ7ORq9GU78Dr8+CR4dYcRPYG5V9e2bmsG987UCXltmVn17Sp2kErqiVBffgZB7HZY/Cg394JH/aSNVqotHB+j4JOxbDBcPV992lTpDJXRFqS7e3cHSHpx9YdRKsHao/n30fgvqucDvL1S8foxS56mErijVxdwKxm+DpzZDPWf97MPKXhv6eClcG0GjKMWohK4o1amBF1ja6ncfbYZB817w93tw7dbZIJU7mUroilLbCAH3fAS6PG3Ui6IUUgldUWojx2bQ/RVtXHrUX7dfXrkjqISuKLVVl+e1E7DrXobcTGNHo9QAKqErSm1lZgH3fgyp52HbHGNHo9QAKqErSm3m1Q3ajoRdn2pzkyp3NDNjB1BcXl4ecXFxZGdnGzsUxcisrKzw8PDA3LyKl8zfCfrM0EoOrBqvzYLk0bFyRcCUWq9GJfS4uDhsbW3x8vJCqAPyjiWlJCkpibi4OLy9vY0dTs1Xzwnu/USrHfN1H3BpDe0fg6CHwcbR2NEpBlSjulyys7NxcnJSyfwOJ4TAyclJ/VKriDZD4OWTMGgBWNjAn6/BR76w4kk48482/Z1S59WoFjqgkrkCqOOgUixtocNo7XY5QpuP9OjPELECGnhrrfa2I8HW1diRKnpSo1roiqJUk0b+MHAOvHwKhi4Gu8aw+R2Y5wc/j9TGrqtaMHVOuRK6EKK/EOKUECJaCPGfS9OEED2EEGlCiPDC21vVH6qiKBVmbg1BD8Hj62DiQej8DJzfA8sehE8CYcssSL1g7CiVanLbhC6EMAU+BwYAfsAIIYRfCYtul1K2Lby9W81xGkxsbCz+/v7lXn7MmDGsWLFCjxGVrKJxKgrOPtB3Brx0AoZ/By6+8M8H8EkA/PggXD1p7AiVKipPC70TEC2lPCOlzAV+BgbrNyylNikoUD/daxUzC/AbDKN+g+ePwF2vQtwB+LI77FygumJqsfKcFHUHiv8miwNCSlguVAhxBLgIvCKljLx1ASHEOGAcgKenZ5k7fef3SI5frN6ptvwa2/H2oDa3XS4/P5/Ro0dz+PBhWrZsyXfffcfcuXP5/fffycrKokuXLnz55Zf/OXH37rvvlrhMjx49CAkJYcuWLaSmpvL1118TFhZGQUEBU6ZM4c8//0QIwVNPPcVzzz3HwYMHeemll7h+/TrOzs4sWbIENzc3Dh48yBNPPIGNjQ3dunUr8zPExsYyatQoMjIyAPjss8/o0qULAHPmzOH777/HxMSEAQMGMHv2bKKjoxk/fjwJCQmYmpryyy+/cOHCBebOncvatWsBmDhxIsHBwYwZMwYvLy+eeOIJ/vrrLyZOnEh6ejqLFy8mNzcXHx8fvv/+e2xsbLhy5Qrjx4/nzJkzACxcuJD169fj7OzM888/D8C0adNwdXVl0qRJFfuDKlXXoKk2X2nHsbD2Rdj4JpxcC0MWglNzY0enVFB5WuglDTe4derxQ0BTKWUQ8CmwqqQNSSkXSymDpZTBLi4uFQrUkE6dOsW4ceM4evQodnZ2fPHFF0ycOJH9+/cTERFBVlZWUZIrrqxl8vPz2bdvH5988gnvvPMOAIsXL+bs2bMcPnyYo0ePMnLkSPLy8njuuedYsWJFUQKfNm0aAI8//jgLFixg9+7dt/0MDRs2ZOPGjRw6dIjly5cXJcv169ezatUq9u7dy5EjR3j11VcBGDlyJM8++yxHjhxh165duLm53XYfVlZW7Nixg4cffphhw4axf/9+jhw5QuvWrfn6668BmDRpEnfddRdHjhzh0KFDtGnThieffJKlS5cCoNPp+Pnnnxk5cuRt96foUf2G8NAPMOz/IOEkLOwKexap4Y61THla6HFAk2LPPdBa4UWklNeKPV4nhPhCCOEspUysbGDlaUnrS5MmTejatSsAjz76KAsWLMDb25s5c+aQmZlJcnIybdq0YdCgQTett2XLllKXGTZsGAAdOnQgNjYWgE2bNjF+/HjMzLQ/g6OjIxEREURERNCnTx9A685wc3MjLS2N1NRU7rrrLgBGjRrF+vXrS/0MeXl5TJw4kfDwcExNTYmKiira5+OPP46NjU3RPtPT04mPj2fo0KGAlqjL46GHHip6HBERwRtvvEFqairXr1+nX79+APz999989913AJiammJvb4+9vT1OTk4cPnyYK1eu0K5dO5ycnMq1T0WPhIDA4eAVBr9Pgg1TtPlRh3yu1XlXarzyJPT9QAshhDcQDzwMPFJ8ASFEI+CKlFIKITqhtfyTqjtYQ7m1K0UIwTPPPMOBAwdo0qQJ06dP/89FL9nZ2WUuY2lpCWhJLT8/H9CuiLx1X1JK2rRp859WeGpqaoXGZs+bNw9XV1eOHDmCTqcrStKl7bMkZmZm6Iq10G79zPXq/Ttf5pgxY1i1ahVBQUEsWbKErVu3lhnf2LFjWbJkCZcvX+aJJ54o9+dSDMDOTZsP9fAPsOE1+KKLdjI1+AlVUqCGu22Xi5QyH5gI/AmcAP4npYwUQowXQowvXOwBIKKwD30B8LAsLUvUAufPny9KqD/99FNRf7WzszPXr18vcVTLjWRX1jK36tu3L4sWLSpK8MnJyfj6+pKQkFC0/7y8PCIjI3FwcMDe3p4dO3YA8OOPP5a57bS0NNzc3DAxMeH7778vOnHZt29fvvnmGzIzM4v2aWdnh4eHB6tWrQIgJyeHzMxMmjZtyvHjx8nJySEtLY3NmzeXur/09HTc3NzIy8u7KbbevXuzcOFCQPu1ce2a9mNu6NChbNiwgf379xe15pUaRAhoPwqe2Q1NOsIfL8H3QyEtztiRKWUo1zh0KeU6KWVLKWVzKeXMwtcWSSkXFT7+TErZRkoZJKXsLKXcpc+g9a1169YsXbqUwMBAkpOTmTBhAk899RQBAQEMGTKEjh07/mcdBweH2y5zq7Fjx+Lp6UlgYCBBQUEsW7YMCwsLVqxYwZQpUwgKCqJt27bs2qV9nd9++y3PPvssoaGhWFtbl7ntZ555hqVLl9K5c2eioqKKWtP9+/fnvvvuIzg4mLZt2zJ37lwAvv/+exYsWEBgYCBdunTh8uXLNGnShOHDhxMYGMjIkSNp165dqfubMWMGISEh9OnTh1atWhW9Pn/+fLZs2UJAQAAdOnQgMlI7V25hYUHPnj0ZPnw4pqamt/2uFCNxaAKjVmkzJF3YB1+Eai332tteq9OEsRrSwcHB8sCBAze9duLECVq3bm2UeBTD0ul0tG/fnl9++YUWLVqUuIw6HmqY5LOweiKc2wEt+sGg+Vr3jGJQQoiDUsrgkt5Tl/4rBnf8+HF8fHzo3bt3qclcqYEcvWH079B/NpzdBl90hqP/U631GqTGFedSKubPP/9kypQpN73m7e3NypUrjRTR7fn5+RWNS1dqGRMT6DwBfPrAqgnw21PavKaDFmhlfBWjUgm9luvXr586qagYnrMPPLEBdn8Gf78HX98NI1eoi5GMTHW5KIpSOSam0PV5GPMHZKVqk2tc2GfsqO5oKqErilI1TTrB2E1gZQ9LB8HxNcaO6I6lErqiKFXn1Bye3AiNAuB/j8HuL4wd0R1JJXRFUapHPWdtFEzre7Up8NZPUZUbDUwl9Gq0detW7r33XmOHUe3WrFnD7NmzS33/wIEDqlKiojG3hgeXQudnYe8irbWem2nsqO4YNXeUy/qpcPlY9W6zUQAMKD0x1TQFBQV6uYqyotu97777uO+++0p9Pzg4mODgEq9zUO5EJqbQ/31w8IQNU7V+9RE/Q/2aW2G1rlAt9FvExsbSqlUrxo4di7+/PyNHjmTTpk107dqVFi1asG/fPvbt20eXLl1o164dXbp04dSpU//ZTkBAAKmpqUgpcXJyKqo4OGrUKDZt2kRsbCxhYWG0b9+e9u3bF13ev3XrVnr27MkjjzxCQEAABQUFTJ48mY4dOxIYGMiXX35Zauxbt26le/fuDB06FD8/P8aPH19UXKt+/fq89dZbhISEsHv3bn744Qc6depE27Ztefrpp4tqvWzYsIH27dsTFBRE7969AViyZAkTJ04E4JdffsHf35+goCC6d+9etN8bv0ySk5MZMmQIgYGBdO7cmaNHjwIwffp0nnjiCXr06EGzZs1YsGBBlf9WSg3XeTw89D1cidCGNSZGGzuiuk9KaZRbhw4d5K2OHz/+n9cM7ezZs9LU1FQePXpUFhQUyPbt28vHH39c6nQ6uWrVKjl48GCZlpYm8/LypJRSbty4UQ4bNkxKKeWWLVvkPffcI6WU8umnn5Zr166Vx44dk8HBwXLs2LFSSil9fHxkenq6zMjIkFlZWVJKKaOiouSN72PLli3SxsZGnjlzRkop5ZdffilnzJghpZQyOztbdujQoei9W23ZskVaWlrKmJgYmZ+fL++++275yy+/SCmlBOTy5cullNr3fO+998rc3FwppZQTJkyQS5culVevXpUeHh5F209KSpJSSvntt9/KZ599Vkoppb+/v4yLi5NSSpmSkvKfzz1x4kQ5ffp0KaWUmzdvlkFBQVJKKd9++20ZGhoqs7OzZUJCgnR0dCzaf2lqwvGgVIPz+6T8wFvK2U2lPLfb2NHUesABWUperbldLkbk7e1NQEAAAG3atKF3794IIQgICCA2Npa0tDRGjx7N6dOnEUKQl5f3n22EhYWxbds2mjZtyoQJE1i8eDHx8fE4OjpSv3590tLSSqxXDtCpUye8vb0B+Ouvvzh69GhR9ca0tDROnz5d9P6tOnXqRLNmzQAYMWIEO3bs4IEHHsDU1JT7778fgM2bN3Pw4MGiAmJZWVk0bNiQPXv20L1796JtOzo6/mf7Xbt2ZcyYMQwfPryoxntxO3bs4NdffwWgV69eJCUlkZaWBsA999yDpaUllpaWNGzYkCtXruDh4XG7P4dS2zXpqA1r/OEBWHofDPsS2gw1dlR1kkroJbhRuxzAxMSk6LmJiQn5+fm8+eab9OzZk5UrVxIbG0uPHj3+s43u3bvz+eefc/78eWbOnMnKlStZsWIFYWFhQOn1yuHmOuNSSj799NNyXw1aUi130CatuNFvLqVk9OjRzJo166Zl16xZc9ua64sWLWLv3r388ccftG3blvDw8JvelyXU9bixzeLfa/G68ModwLGZNqzx5xHwyxitDG/oRFVfvZqpPvRKSEtLw93dHdD6l0vSpEkTEhMTOX36NM2aNaNbt27MnTu3KKGXVq/8Vv369WPhwoVFvwKioqKK5gktyb59+zh79iw6nY7ly5eXOPdo7969WbFiBVevXgW0fu9z584RGhrKP//8w9mzZ4tev1VMTAwhISG8++67ODs7c+HChZve7969e1E99K1bt+Ls7IydnV2p8Sp3kHpO8NhqbYLqv96A9a+qYY3VTCX0Snj11Vd57bXX6Nq1a5kz3oeEhNCyZUtA64KJj48vSrCl1Su/1dixY/Hz86N9+/b4+/vz9NNPl9myDQ0NZerUqfj7++Pt7V00rVxxfn5+vPfee/Tt25fAwED69OnDpUuXcHFxYfHixQwbNoygoKCbppi7YfLkyQQEBODv70/37t0JCgq66f3p06dz4MABAgMDmTp1atHcoYoCaMMaH1gCXZ6DfYth+aOQW3oDRakYVQ+9Dtm6dStz584tcQLr2kgdD3Xcvv/TWunOvlq/ulvQ7ddRVD10RVFqoE5PaRUas1Lg/3rBPx9CgTqvUhXqpGgtdOzYMUaNGnXTa5aWluzdu7fEE7SKUmP59NbmLV33Cmx5D6I2wNBF4KwmPqkMldBroYCAgP+MLlGUWsvGER74BlrdA3+8DIvCoM870PEpbUINpdzUt6UoSs3gfz9M2A1e3bS+9e+HaMMblXJTCV1RlJrDzg1G/qJNQB13AL4IhfBlat7SclIJXVGUmkUI6DAGJuwEV39t7tLlj8L1hOrZfkGeNrNS5Ko6VwlS9aErilIzOXrDmLWw+3P4ewZ80VlrubeuYInq/ByIPwixO+HcDi2Z5xUmcusGEPwkdBoHtq7V/xkMTLXQq1FdrYc+ffp05s6dC8CYMWOK6sooit6ZmELXSTDuH7BrDMtHwsoJkJ1W+jq5mXDmH9jyPnx7D8xqAt8O0EbRZCRCu1FazfZRq8CzC2z/CD7xh1XPwpVIg300faixLfQP9n3AyeST1brNVo6tmNJpSrVuU5+qUg9dX7XUFcUoXP1g7GbYNge2fwxnt8GQz6FZD8i5Dhf2wrmdWis8/iDo8kCYaHMgdBwLXl3BM1QbUVNc856QFAN7voDDP0L4D9C8l1ZnpnmvWldrplwtdCFEfyHEKSFEtBBiahnLdRRCFAghHqi+EA2rttdDL++6c+bMISAggKCgIKZO1f6k//d//0fHjh0JCgri/vvvJzOzbvUvKrWcmQX0egOe/AvMreC7wbCwK8z2hB+GwY5PtETeeQI88j+YEgtPb9Mm22h1z3+T+Q1OzeGej+Cl49DrTa2V/sMwWNgFDv+gddnUFqXV1b1xA0yBGKAZYAEcAfxKWe5vYB3wwO22q+qh66ceennWXbdunQwNDZUZGRlSyn/rnicmJhZta9q0aXLBggVSSq2W+YcffiillHL06NFFNdb1rSYcD0oNlZMh5YbXpfxmoJQbp0t5eqOU2deqZ9t52VIe+kHKz0OlfNtOyjk+Um6dI2VGUvVsv4qoYj30TkC0lPIMgBDiZ2AwcPyW5Z4DfgU6VvU/GWOr7fXQb7fupk2bePzxx7GxsQH+rXseERHBG2+8QWpqKtevXy93yV5FMTgLG+g3Uz/bNrOEdiOh7SNwZgvs+kzrf9/+kfZa52fA2Uc/+66i8iR0d6B4jdQ4IKT4AkIId2Ao0Is6kNBrcz308qy7YcOGEuuejxkzhlWrVhEUFMSSJUvYunVrufapKHWSEFo/evNecOU47PkcDn8PB76Blv3AvQPYe4Cd+7/35la3364elSehl3RW4NZR/p8AU6SUBWVNkCCEGAeMA/D09CxniDVPReqh5+bm3lQP/bPPPivahoeHByYmJixduvS29dB79eqFubk5UVFRuLu7l1putzzr9u3bl3fffZdHHnkEGxsbkpOTcXR0JD09HTc3N/Ly8vjxxx+LPqOi3PFc/WDw59D7ba1KZPiPWt2ZW9Vz+TfB37jZuYN9E7B3h/qu2sgdPSlPQo8DmhR77gFcvGWZYODnwmTuDAwUQuRLKVcVX0hKuRhYDFr53ErGbHSvvvoqo0eP5uOPP6ZXr16lLhcSElKUqMPCwnjttdduqod+//3388svv9CzZ88y66HHxsbSvn17pJS4uLiwatWqcsVZ2rr9+/cnPDyc4OBgLCwsGDhwIO+//z4zZswgJCSEpk2bEhAQQHp6esW+GEWp6+o3hF7TtFteNqRf1MoTpMUX3l+Aa/GQFA1ntkLu9ZvXNzED28YQMk6rCV/NblsPXQhhBkQBvYF4YD/wiJSyxAGbQoglwFopZZmDlVU9dOV21PGg1GpSauPlr91I9oW3a/Hg0wcCH6zUZsuqh37bFrqUMl8IMRH4E20kyzdSykghxPjC9xdVKipFUZS6TAiwdtBurm0MsstyXVgkpVyHNhyx+GslJnIp5Ziqh6WUpax66Iqi3Llq3JWiUsrbzjx/p7sT6qHfritQUZT/qlG1XKysrEhKSlL/mO9wUkqSkpJuGsqpKMrt1agWuoeHB3FxcSQkVFOZTKXWsrKywsPDw9hhKEqtUqMSurm5ealXQCqKoihlq1FdLoqiKErlqYSuKIpSR6iEriiKUkfc9kpRve1YiATgXCVXdwYSqzGc6lJT44KaG5uKq2JUXBVTF+NqKqV0KekNoyX0qhBCHCjt0ldjqqlxQc2NTcVVMSquirnT4lJdLoqiKHWESuiKoih1RG1N6IuNHUApampcUHNjU3FVjIqrYu6ouGplH7qiKIryX7W1ha4oiqLcQiV0RVGUOqJGJ3QhRKwQ4pgQIlwIcaCE94UQYoEQIloIcVQI0d4AMfkWxnPjdk0I8cIty/QQQqQVW+YtPcXyjRDiqhAiothrjkKIjUKI04X3DUpZt78Q4lThdzfVAHF9KIQ4Wfh3WimEcChl3TL/5nqKbboQIr7Y32tgKesa+jtbXiymWCFEeCnr6uU7E0I0EUJsEUKcEEJECiGeL3zdqMdYGXEZ9RgrIy7DHV9Syhp7A2IB5zLeHwisR5vIujOw18DxmQKX0Qb6F3+9B9o0fPref3egPRBR7LU5wNTCx1OBD0qJOwZoBlgARwA/PcfVFzArfPxBSXGV52+up9imA6+U429t0O/slvc/At4y5HcGuAHtCx/bok1F6WfsY6yMuIx6jJURl8GOrxrdQi+HwcB3UrMHcBBCuBlw/72BGCllZa94rRIp5TYg+ZaXBwNLCx8vBYaUsGonIFpKeUZKmQv8XLie3uKSUv4lpcwvfLoHbbJxgyvlOysPg39nNwhtxpfhwE/Vtb9yxnRJSnmo8HE6cAJwx8jHWGlxGfsYK+P7Ko9q+b5qekKXwF9CiINCiHElvO8OXCj2PI7yf4HV4WFK/0cWKoQ4IoRYL4QwzISCGlcp5SXQDjCgYQnLGPt7ewLtl1VJbvc315eJhT/VvymlC8GY31kYcEVKebqU9/X+nQkhvIB2wF5q0DF2S1zFGfUYKyEugxxfNT2hd5VStgcGAM8KIbrf8n5Jc9UZZBymEMICuA/4pYS3D6F1wwQBnwKrDBFTBRjze5sG5AM/lrLI7f7m+rAQaA60BS6hdW/cymjfGTCCslvnev3OhBD1gV+BF6SU18q7WgmvVev3VVpcxj7GSojLYMdXjU7oUsqLhfdXgZVoP0uKiwOaFHvuAVw0THQMAA5JKa/c+oaU8pqU8nrh43WAuRDC2UBxXbnR7VR4f7WEZYzyvQkhRgP3AiNlYcfhrcrxN692UsorUsoCKaUO+L9S9mms78wMGAYsL20ZfX5nQghztOT0o5Tyt8KXjX6MlRKX0Y+xkuIy5PFVYxO6EKKeEML2xmO0Ex4Rtyy2BnhMaDoDaTd+ChpAqa0mIUSjwn5PhBCd0L7nJAPFtQYYXfh4NLC6hGX2Ay2EEN6FvzQeLlxPb4QQ/YEpwH1SysxSlinP31wfsRU/7zK0lH0a/DsrdDdwUkoZV9Kb+vzOCo/hr4ETUsqPi71l1GOstLiMfYyVEZfhjq/qPtNbXTe0s71HCm+RwLTC18cD4wsfC+BztLPDx4BgA8Vmg5ag7Yu9VjyuiYUxH0E7OdNFT3H8hPYTLg/tf/gnASdgM3C68N6xcNnGwLpi6w5EOwsfc+O71XNc0Wh9hOGFt0W3xlXa39wAsX1fePwcLfxH5FYTvrPC15fcOK6KLWuQ7wzohvaz/2ixv9tAYx9jZcRl1GOsjLgMdnypS/8VRVHqiBrb5aIoiqJUjEroiqIodYRK6IqiKHWESuiKoih1hEroiqIodYRK6IqiKHWESuiKoih1xP8DWO/xTqlHmpIAAAAASUVORK5CYII=\n", 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" ] diff --git a/Model Training/DNN_and_Visualization_with_hash.ipynb b/Model Training/DNN_and_Visualization_with_hash.ipynb index b4b854a..43f5e6d 100644 --- a/Model Training/DNN_and_Visualization_with_hash.ipynb +++ b/Model Training/DNN_and_Visualization_with_hash.ipynb @@ -80,12 +80,17 @@ "metadata": {}, "outputs": [], "source": [ - "X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", + "# covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", + "\n", + "X = pd.read_csv('../data/Training_Data/Training_Dataset_with_threshold.csv')\n", "repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", "covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", - "y = pd.read_csv('../data/Training_Data/Labels.csv')\n", - "repackaged_benign_test_y = pd.read_csv('../data/Test_Data/Labels_Repackaged_Benign_Test.csv')\n", - "COVID_test_y = pd.read_csv('../data/Test_Data/Labels_COVID_Test.csv')" + "\n", + "y = pd.read_csv('../data/Training_Data/Labels_trainingset.csv')\n", + "repackaged_benign_test_y = pd.read_csv('../data/Test_Data/Labels_testset.csv')\n", + "COVID_test_y = pd.read_csv('../data/Test_Data/Labels_COVID_testset.csv')" ] }, { @@ -102,7 +107,7 @@ " 'Family_Name', 'Malware_Category', 'Malware/Benign','sdkVersion', 'targetSdkVersion'], axis = 1, inplace = True)\n", "\n", "covid_test_X.drop(['Package_Name', 'SHA256', 'ThumbPrint', 'Application_Category', \n", - " 'Family_Name', 'Malware_Category', 'Malware/Benign','sdkVersion', 'targetSdkVersion'], axis = 1, inplace = True)\n" + " 'Family_Name', 'Malware_Category', 'Malware/Benign','sdkVersion', 'targetSdkVersion'], axis = 1, inplace = True)" ] }, { @@ -121,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -141,7 +146,18 @@ { "data": { "text/plain": [ - "Series([], Name: count, dtype: float64)" + "Permission: FACTORY_TEST 9538.0\n", + "Permission: DUMP 9538.0\n", + "Permission: BATTERY_STATS 9538.0\n", + "Permission: BIND_WALLPAPER 9538.0\n", + "Permission: BIND_INPUT_METHOD 9538.0\n", + " ... \n", + "rotation_vector 9538.0\n", + "temperature 9538.0\n", + "if_the_app_using_suspicious_libs 9538.0\n", + "malwareNum 9538.0\n", + "proportion 9538.0\n", + "Name: count, Length: 335, dtype: float64" ] }, "execution_count": 5, @@ -163,29 +179,23 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "X['label']=y['label']" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ + "X['label']=y['label']\n", "# Create dataset without sensor features in order to see if there's some improvement by adding sensor features\n", - "sensor_lst=list(X.iloc[:,-32:-3].columns)\n", + "sensor_lst=list(X.iloc[:,-14:-3].columns)\n", + "# sensor_lst.remove('if_the_app_using_suspicious_libs')\n", "X_wo_sensors = X.drop(sensor_lst, axis = 1)\n", + "\n", "repackaged_benign_test_X_wo_sensors = repackaged_benign_test_X.drop(sensor_lst, axis = 1)\n", "covid_test_X_wo_sensors = covid_test_X.drop(sensor_lst, axis = 1)\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -389,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -421,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -447,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -458,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -488,7 +498,7 @@ { "data": { "text/html": [ - " |-Trial ID: 46f49e42b566c459ef09f302d416834a" + " |-Trial ID: 627032c51d3b17641810230698085038" ], "text/plain": [ "" @@ -500,7 +510,7 @@ { "data": { "text/html": [ - " |-Score: 1.0" + " |-Score: 0.9960784316062927" ], "text/plain": [ "" @@ -536,7 +546,7 @@ { "data": { "text/html": [ - " |-learning_rate: 0.0001" + " |-learning_rate: 0.01" ], "text/plain": [ "" @@ -626,14 +636,14 @@ } ], "source": [ - "tuner.search(X_train_resample.drop(['proportion','label'],axis=1).values, y_train_resample.values, epochs = 20, \n", - " validation_data=(X_test.drop(['proportion','label'],axis=1).values, y_test.values),verbose=2, \n", + "tuner.search(X_train_resample.drop(['malwareNum','proportion','label'],axis=1).values, y_train_resample.values, epochs = 20, \n", + " validation_data=(X_test.drop(['malwareNum','proportion','label'],axis=1).values, y_test.values),verbose=2, \n", " callbacks = [ClearTrainingOutput()])" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -643,9 +653,9 @@ "\n", "The hyperparameter search is complete. \n", "\n", - "The optimal number of units in the first densely-connected layer is 64 \n", + "The optimal number of units in the first densely-connected layer is 32 \n", "\n", - "The optimal number of units in the second densely-connected layer is 16 \n", + "The optimal number of units in the second densely-connected layer is 48 \n", "\n", "The optimal learning rate for the optimizer\n", "is 0.0001.\n", @@ -662,14 +672,12 @@ "The optimal number of units in the second densely-connected layer is {best_hps.get('units2')} \\n\n", "The optimal learning rate for the optimizer\n", "is {best_hps.get('learning_rate')}.\n", - "\"\"\")\n", - "\n", - "\n" + "\"\"\")" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -699,7 +707,7 @@ { "data": { "text/html": [ - " |-Trial ID: 17be068b8be392e95a4a9d7ad288c694" + " |-Trial ID: 86fec3cd61894a8f98d8cf5f126d9a53" ], "text/plain": [ "" @@ -747,7 +755,7 @@ { "data": { "text/html": [ - " |-learning_rate: 0.01" + " |-learning_rate: 0.0001" ], "text/plain": [ "" @@ -819,7 +827,7 @@ { "data": { "text/html": [ - " |-units2: 16" + " |-units2: 48" ], "text/plain": [ "" @@ -855,14 +863,14 @@ "# project_name='2_layers',\n", "# overwrite = True) \n", "\n", - "tuner.search(X_train_wo_sensor_resample.drop(['proportion','label'],axis=1).values,y_train_wo_sensor_resample.values, epochs = 20,\n", + "tuner.search(X_train_wo_sensor_resample.drop(['malwareNum','proportion','label'],axis=1).values,y_train_wo_sensor_resample.values, epochs = 20,\n", " validation_split=0.2,verbose=2,\n", " callbacks = [ClearTrainingOutput()])" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -872,9 +880,9 @@ "\n", "The hyperparameter search is complete. \n", "\n", - "The optimal number of units in the first densely-connected layer is 48 \n", + "The optimal number of units in the first densely-connected layer is 64 \n", "\n", - "The optimal number of units in the second densely-connected layer is 64 \n", + "The optimal number of units in the second densely-connected layer is 48 \n", "\n", "The optimal learning rate for the optimizer\n", "is 0.0001.\n", @@ -903,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1022,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1033,18 +1041,18 @@ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_1 (Dense) (None, 64) 22656 \n", + "dense_1 (Dense) (None, 32) 10688 \n", "_________________________________________________________________\n", - "dropout_1 (Dropout) (None, 64) 0 \n", + "dropout_1 (Dropout) (None, 32) 0 \n", "_________________________________________________________________\n", - "dense_2 (Dense) (None, 16) 1040 \n", + "dense_2 (Dense) (None, 48) 1584 \n", "_________________________________________________________________\n", - "dropout_2 (Dropout) (None, 16) 0 \n", + "dropout_2 (Dropout) (None, 48) 0 \n", "_________________________________________________________________\n", - "dense_3 (Dense) (None, 1) 17 \n", + "dense_3 (Dense) (None, 1) 49 \n", "=================================================================\n", - "Total params: 23,713\n", - "Trainable params: 23,713\n", + "Total params: 12,321\n", + "Trainable params: 12,321\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -1054,9 +1062,9 @@ "tf.keras.backend.clear_session()\n", "model = keras.Sequential()\n", "model = Sequential()\n", - "model.add(Dense(64, input_dim=X_train.shape[1]-2, activation='relu'))\n", + "model.add(Dense(32, input_dim=X_train_resample.drop(['malwareNum','proportion','label'],axis=1).shape[1], activation='relu'))\n", "model.add(Dropout(0.4))\n", - "model.add(Dense(16, activation='relu'))\n", + "model.add(Dense(48, activation='relu'))\n", "model.add(Dropout(0.4))\n", "# model.add(Dense(128, activation='relu'))\n", "model.add(Dense(1, activation='sigmoid'))\n", @@ -1068,221 +1076,221 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train on 13162 samples, validate on 2230 samples\n", + "Train on 10848 samples, validate on 1908 samples\n", "Epoch 1/100\n", - " - 5s - loss: 0.6362 - accuracy: 0.6255 - auc: 0.6253 - val_loss: 0.5429 - val_accuracy: 0.7184 - val_auc: 0.6920\n", + " - 3s - loss: 0.6530 - accuracy: 0.6037 - auc: 0.5965 - val_loss: 0.5537 - val_accuracy: 0.8234 - val_auc: 0.6774\n", "Epoch 2/100\n", - " - 2s - loss: 0.5346 - accuracy: 0.7361 - auc: 0.7344 - val_loss: 0.4583 - val_accuracy: 0.7996 - val_auc: 0.7644\n", + " - 2s - loss: 0.5556 - accuracy: 0.7241 - auc: 0.7233 - val_loss: 0.4616 - val_accuracy: 0.8103 - val_auc: 0.7589\n", "Epoch 3/100\n", - " - 2s - loss: 0.4839 - accuracy: 0.7731 - auc: 0.7847 - val_loss: 0.4369 - val_accuracy: 0.7919 - val_auc: 0.8004\n", + " - 2s - loss: 0.4785 - accuracy: 0.7798 - auc: 0.7830 - val_loss: 0.4054 - val_accuracy: 0.8155 - val_auc: 0.8040\n", "Epoch 4/100\n", - " - 3s - loss: 0.4494 - accuracy: 0.7969 - auc: 0.8127 - val_loss: 0.3906 - val_accuracy: 0.8027 - val_auc: 0.8233\n", + " - 2s - loss: 0.4248 - accuracy: 0.8107 - auc: 0.8192 - val_loss: 0.3754 - val_accuracy: 0.8187 - val_auc: 0.8315\n", "Epoch 5/100\n", - " - 3s - loss: 0.4218 - accuracy: 0.8107 - auc: 0.8322 - val_loss: 0.3742 - val_accuracy: 0.8076 - val_auc: 0.8396\n", + " - 2s - loss: 0.3888 - accuracy: 0.8249 - auc: 0.8413 - val_loss: 0.3523 - val_accuracy: 0.8333 - val_auc: 0.8503\n", "Epoch 6/100\n", - " - 2s - loss: 0.4006 - accuracy: 0.8195 - auc: 0.8458 - val_loss: 0.3745 - val_accuracy: 0.8090 - val_auc: 0.8518\n", + " - 2s - loss: 0.3617 - accuracy: 0.8403 - auc: 0.8577 - val_loss: 0.3409 - val_accuracy: 0.8386 - val_auc: 0.8643\n", "Epoch 7/100\n", - " - 3s - loss: 0.3887 - accuracy: 0.8264 - auc: 0.8566 - val_loss: 0.3765 - val_accuracy: 0.8000 - val_auc: 0.8608\n", + " - 2s - loss: 0.3506 - accuracy: 0.8442 - auc: 0.8700 - val_loss: 0.3132 - val_accuracy: 0.8454 - val_auc: 0.8748\n", "Epoch 8/100\n", - " - 2s - loss: 0.3743 - accuracy: 0.8309 - auc: 0.8645 - val_loss: 0.3516 - val_accuracy: 0.8211 - val_auc: 0.8683\n", + " - 2s - loss: 0.3314 - accuracy: 0.8520 - auc: 0.8794 - val_loss: 0.3171 - val_accuracy: 0.8375 - val_auc: 0.8834\n", "Epoch 9/100\n", - " - 3s - loss: 0.3623 - accuracy: 0.8364 - auc: 0.8715 - val_loss: 0.3452 - val_accuracy: 0.8184 - val_auc: 0.8747\n", + " - 2s - loss: 0.3162 - accuracy: 0.8616 - auc: 0.8873 - val_loss: 0.3079 - val_accuracy: 0.8401 - val_auc: 0.8908\n", "Epoch 10/100\n", - " - 2s - loss: 0.3556 - accuracy: 0.8426 - auc: 0.8775 - val_loss: 0.3587 - val_accuracy: 0.8108 - val_auc: 0.8801\n", + " - 2s - loss: 0.3070 - accuracy: 0.8661 - auc: 0.8940 - val_loss: 0.2913 - val_accuracy: 0.8627 - val_auc: 0.8970\n", "Epoch 11/100\n", - " - 3s - loss: 0.3406 - accuracy: 0.8504 - auc: 0.8826 - val_loss: 0.3294 - val_accuracy: 0.8390 - val_auc: 0.8851\n", + " - 2s - loss: 0.2988 - accuracy: 0.8658 - auc: 0.8997 - val_loss: 0.2769 - val_accuracy: 0.8732 - val_auc: 0.9023\n", "Epoch 12/100\n", - " - 3s - loss: 0.3364 - accuracy: 0.8528 - auc: 0.8873 - val_loss: 0.3262 - val_accuracy: 0.8395 - val_auc: 0.8895\n", + " - 2s - loss: 0.2937 - accuracy: 0.8741 - auc: 0.9045 - val_loss: 0.2846 - val_accuracy: 0.8622 - val_auc: 0.9068\n", "Epoch 13/100\n", - " - 2s - loss: 0.3320 - accuracy: 0.8555 - auc: 0.8914 - val_loss: 0.3184 - val_accuracy: 0.8426 - val_auc: 0.8932\n", + " - 2s - loss: 0.2811 - accuracy: 0.8787 - auc: 0.9088 - val_loss: 0.2811 - val_accuracy: 0.8632 - val_auc: 0.9109\n", "Epoch 14/100\n", - " - 2s - loss: 0.3218 - accuracy: 0.8596 - auc: 0.8950 - val_loss: 0.3109 - val_accuracy: 0.8448 - val_auc: 0.8967\n", + " - 2s - loss: 0.2767 - accuracy: 0.8794 - auc: 0.9127 - val_loss: 0.2849 - val_accuracy: 0.8632 - val_auc: 0.9144\n", "Epoch 15/100\n", - " - 2s - loss: 0.3156 - accuracy: 0.8651 - auc: 0.8984 - val_loss: 0.3109 - val_accuracy: 0.8453 - val_auc: 0.8999\n", + " - 2s - loss: 0.2715 - accuracy: 0.8873 - auc: 0.9160 - val_loss: 0.2661 - val_accuracy: 0.8648 - val_auc: 0.9176\n", "Epoch 16/100\n", - " - 2s - loss: 0.3095 - accuracy: 0.8680 - auc: 0.9014 - val_loss: 0.3184 - val_accuracy: 0.8466 - val_auc: 0.9028\n", + " - 2s - loss: 0.2662 - accuracy: 0.8877 - auc: 0.9191 - val_loss: 0.2669 - val_accuracy: 0.8679 - val_auc: 0.9205\n", "Epoch 17/100\n", - " - 2s - loss: 0.3059 - accuracy: 0.8682 - auc: 0.9042 - val_loss: 0.3146 - val_accuracy: 0.8457 - val_auc: 0.9055\n", + " - 2s - loss: 0.2597 - accuracy: 0.8900 - auc: 0.9218 - val_loss: 0.2708 - val_accuracy: 0.8700 - val_auc: 0.9231\n", "Epoch 18/100\n", - " - 2s - loss: 0.2981 - accuracy: 0.8738 - auc: 0.9067 - val_loss: 0.3122 - val_accuracy: 0.8453 - val_auc: 0.9079\n", + " - 2s - loss: 0.2530 - accuracy: 0.8945 - auc: 0.9244 - val_loss: 0.2571 - val_accuracy: 0.8753 - val_auc: 0.9256\n", "Epoch 19/100\n", - " - 2s - loss: 0.2969 - accuracy: 0.8746 - auc: 0.9091 - val_loss: 0.3121 - val_accuracy: 0.8507 - val_auc: 0.9102\n", + " - 2s - loss: 0.2475 - accuracy: 0.8961 - auc: 0.9268 - val_loss: 0.2653 - val_accuracy: 0.8700 - val_auc: 0.9279\n", "Epoch 20/100\n", - " - 2s - loss: 0.2950 - accuracy: 0.8775 - auc: 0.9112 - val_loss: 0.3077 - val_accuracy: 0.8534 - val_auc: 0.9122\n", + " - 2s - loss: 0.2453 - accuracy: 0.8934 - auc: 0.9289 - val_loss: 0.2624 - val_accuracy: 0.8711 - val_auc: 0.9299\n", "Epoch 21/100\n", - " - 2s - loss: 0.2866 - accuracy: 0.8796 - auc: 0.9132 - val_loss: 0.3133 - val_accuracy: 0.8511 - val_auc: 0.9141\n", + " - 2s - loss: 0.2435 - accuracy: 0.8968 - auc: 0.9308 - val_loss: 0.2536 - val_accuracy: 0.8800 - val_auc: 0.9317\n", "Epoch 22/100\n", - " - 2s - loss: 0.2825 - accuracy: 0.8828 - auc: 0.9150 - val_loss: 0.3007 - val_accuracy: 0.8610 - val_auc: 0.9159\n", + " - 2s - loss: 0.2379 - accuracy: 0.8988 - auc: 0.9326 - val_loss: 0.2557 - val_accuracy: 0.8789 - val_auc: 0.9335\n", "Epoch 23/100\n", - " - 3s - loss: 0.2823 - accuracy: 0.8838 - auc: 0.9168 - val_loss: 0.3061 - val_accuracy: 0.8570 - val_auc: 0.9176\n", + " - 2s - loss: 0.2353 - accuracy: 0.9001 - auc: 0.9342 - val_loss: 0.2605 - val_accuracy: 0.8753 - val_auc: 0.9350\n", "Epoch 24/100\n", - " - 4s - loss: 0.2759 - accuracy: 0.8861 - auc: 0.9184 - val_loss: 0.3108 - val_accuracy: 0.8592 - val_auc: 0.9192\n", + " - 2s - loss: 0.2314 - accuracy: 0.9028 - auc: 0.9358 - val_loss: 0.2479 - val_accuracy: 0.8784 - val_auc: 0.9366\n", "Epoch 25/100\n", - " - 3s - loss: 0.2761 - accuracy: 0.8852 - auc: 0.9199 - val_loss: 0.2999 - val_accuracy: 0.8610 - val_auc: 0.9207\n", + " - 2s - loss: 0.2279 - accuracy: 0.9050 - auc: 0.9373 - val_loss: 0.2529 - val_accuracy: 0.8768 - val_auc: 0.9380\n", "Epoch 26/100\n", - " - 3s - loss: 0.2738 - accuracy: 0.8888 - auc: 0.9214 - val_loss: 0.3049 - val_accuracy: 0.8601 - val_auc: 0.9220\n", + " - 2s - loss: 0.2245 - accuracy: 0.9062 - auc: 0.9387 - val_loss: 0.2465 - val_accuracy: 0.8810 - val_auc: 0.9394\n", "Epoch 27/100\n", - " - 3s - loss: 0.2680 - accuracy: 0.8917 - auc: 0.9227 - val_loss: 0.3062 - val_accuracy: 0.8592 - val_auc: 0.9234\n", + " - 2s - loss: 0.2236 - accuracy: 0.9070 - auc: 0.9400 - val_loss: 0.2465 - val_accuracy: 0.8826 - val_auc: 0.9406\n", "Epoch 28/100\n", - " - 3s - loss: 0.2638 - accuracy: 0.8929 - auc: 0.9240 - val_loss: 0.2926 - val_accuracy: 0.8664 - val_auc: 0.9247\n", + " - 2s - loss: 0.2200 - accuracy: 0.9101 - auc: 0.9412 - val_loss: 0.2462 - val_accuracy: 0.8868 - val_auc: 0.9418\n", "Epoch 29/100\n", - " - 3s - loss: 0.2613 - accuracy: 0.8939 - auc: 0.9252 - val_loss: 0.3028 - val_accuracy: 0.8605 - val_auc: 0.9259\n", + " - 2s - loss: 0.2202 - accuracy: 0.9065 - auc: 0.9423 - val_loss: 0.2494 - val_accuracy: 0.8857 - val_auc: 0.9429\n", "Epoch 30/100\n", - " - 3s - loss: 0.2615 - accuracy: 0.8938 - auc: 0.9264 - val_loss: 0.3028 - val_accuracy: 0.8601 - val_auc: 0.9270\n", + " - 2s - loss: 0.2135 - accuracy: 0.9117 - auc: 0.9434 - val_loss: 0.2519 - val_accuracy: 0.8831 - val_auc: 0.9439\n", "Epoch 31/100\n", - " - 3s - loss: 0.2572 - accuracy: 0.8963 - auc: 0.9275 - val_loss: 0.3014 - val_accuracy: 0.8596 - val_auc: 0.9281\n", + " - 2s - loss: 0.2127 - accuracy: 0.9153 - auc: 0.9445 - val_loss: 0.2403 - val_accuracy: 0.8831 - val_auc: 0.9450\n", "Epoch 32/100\n", - " - 2s - loss: 0.2602 - accuracy: 0.8939 - auc: 0.9286 - val_loss: 0.3041 - val_accuracy: 0.8587 - val_auc: 0.9290\n", + " - 2s - loss: 0.2129 - accuracy: 0.9121 - auc: 0.9454 - val_loss: 0.2435 - val_accuracy: 0.8868 - val_auc: 0.9459\n", "Epoch 33/100\n", - " - 3s - loss: 0.2546 - accuracy: 0.8976 - auc: 0.9295 - val_loss: 0.3036 - val_accuracy: 0.8596 - val_auc: 0.9300\n", + " - 2s - loss: 0.2072 - accuracy: 0.9143 - auc: 0.9464 - val_loss: 0.2390 - val_accuracy: 0.8863 - val_auc: 0.9468\n", "Epoch 34/100\n", - " - 3s - loss: 0.2480 - accuracy: 0.9001 - auc: 0.9305 - val_loss: 0.2964 - val_accuracy: 0.8623 - val_auc: 0.9310\n", + " - 2s - loss: 0.2093 - accuracy: 0.9143 - auc: 0.9472 - val_loss: 0.2424 - val_accuracy: 0.8868 - val_auc: 0.9477\n", "Epoch 35/100\n", - " - 2s - loss: 0.2495 - accuracy: 0.8972 - auc: 0.9314 - val_loss: 0.3014 - val_accuracy: 0.8605 - val_auc: 0.9319\n", + " - 2s - loss: 0.2057 - accuracy: 0.9172 - auc: 0.9481 - val_loss: 0.2403 - val_accuracy: 0.8873 - val_auc: 0.9485\n", "Epoch 36/100\n", - " - 3s - loss: 0.2475 - accuracy: 0.9021 - auc: 0.9323 - val_loss: 0.3055 - val_accuracy: 0.8587 - val_auc: 0.9327\n", + " - 2s - loss: 0.2011 - accuracy: 0.9159 - auc: 0.9489 - val_loss: 0.2406 - val_accuracy: 0.8878 - val_auc: 0.9493\n", "Epoch 37/100\n", - " - 3s - loss: 0.2451 - accuracy: 0.9037 - auc: 0.9331 - val_loss: 0.2986 - val_accuracy: 0.8641 - val_auc: 0.9335\n", + " - 2s - loss: 0.1980 - accuracy: 0.9178 - auc: 0.9497 - val_loss: 0.2405 - val_accuracy: 0.8863 - val_auc: 0.9501\n", "Epoch 38/100\n", - " - 3s - loss: 0.2453 - accuracy: 0.9021 - auc: 0.9339 - val_loss: 0.3073 - val_accuracy: 0.8570 - val_auc: 0.9343\n", + " - 2s - loss: 0.1983 - accuracy: 0.9194 - auc: 0.9504 - val_loss: 0.2406 - val_accuracy: 0.8857 - val_auc: 0.9508\n", "Epoch 39/100\n", - " - 3s - loss: 0.2411 - accuracy: 0.9054 - auc: 0.9347 - val_loss: 0.2990 - val_accuracy: 0.8637 - val_auc: 0.9351\n", + " - 2s - loss: 0.1968 - accuracy: 0.9206 - auc: 0.9512 - val_loss: 0.2388 - val_accuracy: 0.8863 - val_auc: 0.9515\n", "Epoch 40/100\n", - " - 2s - loss: 0.2421 - accuracy: 0.9039 - auc: 0.9355 - val_loss: 0.2998 - val_accuracy: 0.8623 - val_auc: 0.9358\n", + " - 2s - loss: 0.1951 - accuracy: 0.9218 - auc: 0.9518 - val_loss: 0.2407 - val_accuracy: 0.8894 - val_auc: 0.9522\n", "Epoch 41/100\n", - " - 2s - loss: 0.2390 - accuracy: 0.9056 - auc: 0.9362 - val_loss: 0.2991 - val_accuracy: 0.8637 - val_auc: 0.9365\n", + " - 2s - loss: 0.1917 - accuracy: 0.9192 - auc: 0.9525 - val_loss: 0.2369 - val_accuracy: 0.8894 - val_auc: 0.9528\n", "Epoch 42/100\n", - " - 2s - loss: 0.2341 - accuracy: 0.9073 - auc: 0.9368 - val_loss: 0.2990 - val_accuracy: 0.8659 - val_auc: 0.9372\n", + " - 2s - loss: 0.1953 - accuracy: 0.9216 - auc: 0.9531 - val_loss: 0.2385 - val_accuracy: 0.8894 - val_auc: 0.9534\n", "Epoch 43/100\n", - " - 2s - loss: 0.2373 - accuracy: 0.9046 - auc: 0.9375 - val_loss: 0.3068 - val_accuracy: 0.8605 - val_auc: 0.9378\n", + " - 2s - loss: 0.1933 - accuracy: 0.9218 - auc: 0.9537 - val_loss: 0.2349 - val_accuracy: 0.8899 - val_auc: 0.9540\n", "Epoch 44/100\n", - " - 2s - loss: 0.2331 - accuracy: 0.9089 - auc: 0.9381 - val_loss: 0.2996 - val_accuracy: 0.8677 - val_auc: 0.9385\n", + " - 2s - loss: 0.1942 - accuracy: 0.9208 - auc: 0.9543 - val_loss: 0.2366 - val_accuracy: 0.8905 - val_auc: 0.9545\n", "Epoch 45/100\n", - " - 2s - loss: 0.2338 - accuracy: 0.9067 - auc: 0.9388 - val_loss: 0.3063 - val_accuracy: 0.8632 - val_auc: 0.9390\n", + " - 2s - loss: 0.1874 - accuracy: 0.9241 - auc: 0.9548 - val_loss: 0.2356 - val_accuracy: 0.8899 - val_auc: 0.9551\n", "Epoch 46/100\n", - " - 3s - loss: 0.2315 - accuracy: 0.9086 - auc: 0.9393 - val_loss: 0.3029 - val_accuracy: 0.8650 - val_auc: 0.9396\n", + " - 2s - loss: 0.1890 - accuracy: 0.9233 - auc: 0.9554 - val_loss: 0.2357 - val_accuracy: 0.8894 - val_auc: 0.9556\n", "Epoch 47/100\n", - " - 3s - loss: 0.2311 - accuracy: 0.9062 - auc: 0.9399 - val_loss: 0.3027 - val_accuracy: 0.8641 - val_auc: 0.9402\n", + " - 2s - loss: 0.1844 - accuracy: 0.9234 - auc: 0.9559 - val_loss: 0.2358 - val_accuracy: 0.8915 - val_auc: 0.9561\n", "Epoch 48/100\n", - " - 3s - loss: 0.2280 - accuracy: 0.9086 - auc: 0.9405 - val_loss: 0.3045 - val_accuracy: 0.8641 - val_auc: 0.9407\n", + " - 2s - loss: 0.1842 - accuracy: 0.9242 - auc: 0.9564 - val_loss: 0.2379 - val_accuracy: 0.8894 - val_auc: 0.9566\n", "Epoch 49/100\n", - " - 2s - loss: 0.2284 - accuracy: 0.9108 - auc: 0.9410 - val_loss: 0.2978 - val_accuracy: 0.8641 - val_auc: 0.9413\n", + " - 2s - loss: 0.1831 - accuracy: 0.9250 - auc: 0.9569 - val_loss: 0.2324 - val_accuracy: 0.8947 - val_auc: 0.9571\n", "Epoch 50/100\n", - " - 2s - loss: 0.2248 - accuracy: 0.9081 - auc: 0.9415 - val_loss: 0.2944 - val_accuracy: 0.8704 - val_auc: 0.9418\n", + " - 2s - loss: 0.1823 - accuracy: 0.9257 - auc: 0.9574 - val_loss: 0.2329 - val_accuracy: 0.8936 - val_auc: 0.9576\n", "Epoch 51/100\n", - " - 3s - loss: 0.2255 - accuracy: 0.9091 - auc: 0.9420 - val_loss: 0.2977 - val_accuracy: 0.8700 - val_auc: 0.9423\n", + " - 2s - loss: 0.1838 - accuracy: 0.9257 - auc: 0.9578 - val_loss: 0.2360 - val_accuracy: 0.8910 - val_auc: 0.9580\n", "Epoch 52/100\n", - " - 2s - loss: 0.2266 - accuracy: 0.9109 - auc: 0.9425 - val_loss: 0.2995 - val_accuracy: 0.8695 - val_auc: 0.9428\n", + " - 2s - loss: 0.1809 - accuracy: 0.9256 - auc: 0.9583 - val_loss: 0.2338 - val_accuracy: 0.8926 - val_auc: 0.9585\n", "Epoch 53/100\n", - " - 2s - loss: 0.2228 - accuracy: 0.9116 - auc: 0.9430 - val_loss: 0.3062 - val_accuracy: 0.8691 - val_auc: 0.9432\n", + " - 2s - loss: 0.1810 - accuracy: 0.9244 - auc: 0.9587 - val_loss: 0.2415 - val_accuracy: 0.8910 - val_auc: 0.9589\n", "Epoch 54/100\n", - " - 2s - loss: 0.2212 - accuracy: 0.9116 - auc: 0.9434 - val_loss: 0.3106 - val_accuracy: 0.8637 - val_auc: 0.9437\n", + " - 2s - loss: 0.1796 - accuracy: 0.9260 - auc: 0.9591 - val_loss: 0.2368 - val_accuracy: 0.8899 - val_auc: 0.9593\n", "Epoch 55/100\n", - " - 2s - loss: 0.2219 - accuracy: 0.9115 - auc: 0.9439 - val_loss: 0.3094 - val_accuracy: 0.8673 - val_auc: 0.9441\n", + " - 2s - loss: 0.1762 - accuracy: 0.9258 - auc: 0.9595 - val_loss: 0.2357 - val_accuracy: 0.8920 - val_auc: 0.9597\n", "Epoch 56/100\n", - " - 2s - loss: 0.2171 - accuracy: 0.9117 - auc: 0.9443 - val_loss: 0.3008 - val_accuracy: 0.8668 - val_auc: 0.9445\n", + " - 2s - loss: 0.1779 - accuracy: 0.9256 - auc: 0.9599 - val_loss: 0.2378 - val_accuracy: 0.8905 - val_auc: 0.9601\n", "Epoch 57/100\n", - " - 3s - loss: 0.2176 - accuracy: 0.9144 - auc: 0.9448 - val_loss: 0.3094 - val_accuracy: 0.8650 - val_auc: 0.9450\n", + " - 2s - loss: 0.1723 - accuracy: 0.9289 - auc: 0.9602 - val_loss: 0.2356 - val_accuracy: 0.8957 - val_auc: 0.9604\n", "Epoch 58/100\n", - " - 2s - loss: 0.2137 - accuracy: 0.9151 - auc: 0.9452 - val_loss: 0.3044 - val_accuracy: 0.8682 - val_auc: 0.9454\n", + " - 2s - loss: 0.1751 - accuracy: 0.9260 - auc: 0.9606 - val_loss: 0.2352 - val_accuracy: 0.8920 - val_auc: 0.9608\n", "Epoch 59/100\n", - " - 2s - loss: 0.2133 - accuracy: 0.9141 - auc: 0.9456 - val_loss: 0.3021 - val_accuracy: 0.8704 - val_auc: 0.9458\n", + " - 2s - loss: 0.1749 - accuracy: 0.9292 - auc: 0.9610 - val_loss: 0.2351 - val_accuracy: 0.8957 - val_auc: 0.9612\n", "Epoch 60/100\n", - " - 2s - loss: 0.2188 - accuracy: 0.9128 - auc: 0.9460 - val_loss: 0.3002 - val_accuracy: 0.8722 - val_auc: 0.9462\n", + " - 2s - loss: 0.1714 - accuracy: 0.9302 - auc: 0.9613 - val_loss: 0.2378 - val_accuracy: 0.8952 - val_auc: 0.9615\n", "Epoch 61/100\n", - " - 2s - loss: 0.2128 - accuracy: 0.9150 - auc: 0.9464 - val_loss: 0.3107 - val_accuracy: 0.8695 - val_auc: 0.9466\n", + " - 2s - loss: 0.1710 - accuracy: 0.9292 - auc: 0.9617 - val_loss: 0.2363 - val_accuracy: 0.8947 - val_auc: 0.9618\n", "Epoch 62/100\n", - " - 3s - loss: 0.2138 - accuracy: 0.9158 - auc: 0.9468 - val_loss: 0.3071 - val_accuracy: 0.8722 - val_auc: 0.9470\n", + " - 1s - loss: 0.1702 - accuracy: 0.9282 - auc: 0.9620 - val_loss: 0.2353 - val_accuracy: 0.8978 - val_auc: 0.9622\n", "Epoch 63/100\n", - " - 3s - loss: 0.2139 - accuracy: 0.9147 - auc: 0.9471 - val_loss: 0.3024 - val_accuracy: 0.8704 - val_auc: 0.9473\n", + " - 2s - loss: 0.1717 - accuracy: 0.9306 - auc: 0.9623 - val_loss: 0.2390 - val_accuracy: 0.8957 - val_auc: 0.9625\n", "Epoch 64/100\n", - " - 3s - loss: 0.2111 - accuracy: 0.9149 - auc: 0.9475 - val_loss: 0.3026 - val_accuracy: 0.8700 - val_auc: 0.9477\n", + " - 2s - loss: 0.1677 - accuracy: 0.9298 - auc: 0.9626 - val_loss: 0.2336 - val_accuracy: 0.9015 - val_auc: 0.9628\n", "Epoch 65/100\n", - " - 2s - loss: 0.2136 - accuracy: 0.9148 - auc: 0.9479 - val_loss: 0.3016 - val_accuracy: 0.8691 - val_auc: 0.9480\n", + " - 2s - loss: 0.1692 - accuracy: 0.9290 - auc: 0.9629 - val_loss: 0.2329 - val_accuracy: 0.9036 - val_auc: 0.9631\n", "Epoch 66/100\n", - " - 2s - loss: 0.2103 - accuracy: 0.9149 - auc: 0.9482 - val_loss: 0.3113 - val_accuracy: 0.8691 - val_auc: 0.9484\n", + " - 2s - loss: 0.1680 - accuracy: 0.9297 - auc: 0.9632 - val_loss: 0.2404 - val_accuracy: 0.8931 - val_auc: 0.9634\n", "Epoch 67/100\n", - " - 3s - loss: 0.2091 - accuracy: 0.9175 - auc: 0.9485 - val_loss: 0.3032 - val_accuracy: 0.8713 - val_auc: 0.9487\n", + " - 2s - loss: 0.1660 - accuracy: 0.9331 - auc: 0.9635 - val_loss: 0.2399 - val_accuracy: 0.8957 - val_auc: 0.9637\n", "Epoch 68/100\n", - " - 3s - loss: 0.2067 - accuracy: 0.9170 - auc: 0.9488 - val_loss: 0.3075 - val_accuracy: 0.8722 - val_auc: 0.9490\n", + " - 2s - loss: 0.1663 - accuracy: 0.9322 - auc: 0.9638 - val_loss: 0.2345 - val_accuracy: 0.8962 - val_auc: 0.9640\n", "Epoch 69/100\n", - " - 3s - loss: 0.2092 - accuracy: 0.9150 - auc: 0.9492 - val_loss: 0.3118 - val_accuracy: 0.8677 - val_auc: 0.9493\n", + " - 2s - loss: 0.1686 - accuracy: 0.9318 - auc: 0.9641 - val_loss: 0.2354 - val_accuracy: 0.9020 - val_auc: 0.9642\n", "Epoch 70/100\n", - " - 3s - loss: 0.2071 - accuracy: 0.9178 - auc: 0.9495 - val_loss: 0.3073 - val_accuracy: 0.8709 - val_auc: 0.9496\n", + " - 2s - loss: 0.1645 - accuracy: 0.9330 - auc: 0.9643 - val_loss: 0.2380 - val_accuracy: 0.8999 - val_auc: 0.9645\n", "Epoch 71/100\n", - " - 2s - loss: 0.2078 - accuracy: 0.9160 - auc: 0.9498 - val_loss: 0.3141 - val_accuracy: 0.8709 - val_auc: 0.9499\n", + " - 2s - loss: 0.1648 - accuracy: 0.9316 - auc: 0.9646 - val_loss: 0.2418 - val_accuracy: 0.9004 - val_auc: 0.9647\n", "Epoch 72/100\n", - " - 2s - loss: 0.2072 - accuracy: 0.9175 - auc: 0.9501 - val_loss: 0.3099 - val_accuracy: 0.8695 - val_auc: 0.9502\n", + " - 2s - loss: 0.1638 - accuracy: 0.9357 - auc: 0.9649 - val_loss: 0.2375 - val_accuracy: 0.8988 - val_auc: 0.9650\n", "Epoch 73/100\n", - " - 2s - loss: 0.2020 - accuracy: 0.9202 - auc: 0.9504 - val_loss: 0.3110 - val_accuracy: 0.8691 - val_auc: 0.9505\n", + " - 2s - loss: 0.1658 - accuracy: 0.9306 - auc: 0.9651 - val_loss: 0.2387 - val_accuracy: 0.8994 - val_auc: 0.9652\n", "Epoch 74/100\n", - " - 2s - loss: 0.2042 - accuracy: 0.9200 - auc: 0.9506 - val_loss: 0.3103 - val_accuracy: 0.8709 - val_auc: 0.9508\n", + " - 2s - loss: 0.1642 - accuracy: 0.9321 - auc: 0.9654 - val_loss: 0.2396 - val_accuracy: 0.9030 - val_auc: 0.9655\n", "Epoch 75/100\n", - " - 2s - loss: 0.2035 - accuracy: 0.9193 - auc: 0.9509 - val_loss: 0.3167 - val_accuracy: 0.8704 - val_auc: 0.9511\n", + " - 2s - loss: 0.1626 - accuracy: 0.9331 - auc: 0.9656 - val_loss: 0.2416 - val_accuracy: 0.9046 - val_auc: 0.9657\n", "Epoch 76/100\n", - " - 2s - loss: 0.1999 - accuracy: 0.9212 - auc: 0.9512 - val_loss: 0.3184 - val_accuracy: 0.8682 - val_auc: 0.9513\n", + " - 2s - loss: 0.1615 - accuracy: 0.9339 - auc: 0.9658 - val_loss: 0.2432 - val_accuracy: 0.9030 - val_auc: 0.9659\n", "Epoch 77/100\n", - " - 2s - loss: 0.1990 - accuracy: 0.9212 - auc: 0.9515 - val_loss: 0.3175 - val_accuracy: 0.8709 - val_auc: 0.9516\n", + " - 2s - loss: 0.1596 - accuracy: 0.9360 - auc: 0.9661 - val_loss: 0.2421 - val_accuracy: 0.9030 - val_auc: 0.9662\n", "Epoch 78/100\n", - " - 2s - loss: 0.2052 - accuracy: 0.9166 - auc: 0.9517 - val_loss: 0.3148 - val_accuracy: 0.8700 - val_auc: 0.9519\n", + " - 2s - loss: 0.1598 - accuracy: 0.9347 - auc: 0.9663 - val_loss: 0.2494 - val_accuracy: 0.8994 - val_auc: 0.9664\n", "Epoch 79/100\n", - " - 2s - loss: 0.2008 - accuracy: 0.9179 - auc: 0.9520 - val_loss: 0.3185 - val_accuracy: 0.8673 - val_auc: 0.9521\n", + " - 2s - loss: 0.1593 - accuracy: 0.9336 - auc: 0.9665 - val_loss: 0.2428 - val_accuracy: 0.9046 - val_auc: 0.9666\n", "Epoch 80/100\n", - " - 2s - loss: 0.2002 - accuracy: 0.9182 - auc: 0.9522 - val_loss: 0.3190 - val_accuracy: 0.8677 - val_auc: 0.9524\n", + " - 2s - loss: 0.1616 - accuracy: 0.9351 - auc: 0.9667 - val_loss: 0.2407 - val_accuracy: 0.8999 - val_auc: 0.9668\n", "Epoch 81/100\n", - " - 2s - loss: 0.1991 - accuracy: 0.9201 - auc: 0.9525 - val_loss: 0.3213 - val_accuracy: 0.8682 - val_auc: 0.9526\n", + " - 2s - loss: 0.1607 - accuracy: 0.9350 - auc: 0.9669 - val_loss: 0.2422 - val_accuracy: 0.9015 - val_auc: 0.9670\n", "Epoch 82/100\n", - " - 2s - loss: 0.1931 - accuracy: 0.9233 - auc: 0.9527 - val_loss: 0.3133 - val_accuracy: 0.8709 - val_auc: 0.9529\n", + " - 2s - loss: 0.1560 - accuracy: 0.9379 - auc: 0.9671 - val_loss: 0.2391 - val_accuracy: 0.9004 - val_auc: 0.9672\n", "Epoch 83/100\n", - " - 2s - loss: 0.1995 - accuracy: 0.9183 - auc: 0.9530 - val_loss: 0.3201 - val_accuracy: 0.8677 - val_auc: 0.9531\n", + " - 2s - loss: 0.1651 - accuracy: 0.9343 - auc: 0.9673 - val_loss: 0.2471 - val_accuracy: 0.9004 - val_auc: 0.9674\n", "Epoch 84/100\n", - " - 2s - loss: 0.1989 - accuracy: 0.9191 - auc: 0.9532 - val_loss: 0.3184 - val_accuracy: 0.8709 - val_auc: 0.9533\n", + " - 2s - loss: 0.1547 - accuracy: 0.9362 - auc: 0.9675 - val_loss: 0.2436 - val_accuracy: 0.9004 - val_auc: 0.9676\n", "Epoch 85/100\n", - " - 2s - loss: 0.1952 - accuracy: 0.9217 - auc: 0.9534 - val_loss: 0.3285 - val_accuracy: 0.8695 - val_auc: 0.9536\n", + " - 2s - loss: 0.1612 - accuracy: 0.9319 - auc: 0.9677 - val_loss: 0.2438 - val_accuracy: 0.9025 - val_auc: 0.9678\n", "Epoch 86/100\n", - " - 2s - loss: 0.1935 - accuracy: 0.9226 - auc: 0.9537 - val_loss: 0.3221 - val_accuracy: 0.8713 - val_auc: 0.9538\n", + " - 2s - loss: 0.1562 - accuracy: 0.9369 - auc: 0.9679 - val_loss: 0.2449 - val_accuracy: 0.9025 - val_auc: 0.9680\n", "Epoch 87/100\n", - " - 3s - loss: 0.1947 - accuracy: 0.9235 - auc: 0.9539 - val_loss: 0.3268 - val_accuracy: 0.8691 - val_auc: 0.9540\n", + " - 2s - loss: 0.1535 - accuracy: 0.9373 - auc: 0.9681 - val_loss: 0.2450 - val_accuracy: 0.9030 - val_auc: 0.9682\n", "Epoch 88/100\n", - " - 3s - loss: 0.1958 - accuracy: 0.9193 - auc: 0.9541 - val_loss: 0.3186 - val_accuracy: 0.8709 - val_auc: 0.9542\n", + " - 2s - loss: 0.1545 - accuracy: 0.9363 - auc: 0.9683 - val_loss: 0.2450 - val_accuracy: 0.9025 - val_auc: 0.9683\n", "Epoch 89/100\n", - " - 2s - loss: 0.1949 - accuracy: 0.9208 - auc: 0.9543 - val_loss: 0.3221 - val_accuracy: 0.8717 - val_auc: 0.9544\n", + " - 2s - loss: 0.1538 - accuracy: 0.9350 - auc: 0.9684 - val_loss: 0.2493 - val_accuracy: 0.9009 - val_auc: 0.9685\n", "Epoch 90/100\n", - " - 2s - loss: 0.1939 - accuracy: 0.9233 - auc: 0.9545 - val_loss: 0.3232 - val_accuracy: 0.8695 - val_auc: 0.9546\n", + " - 2s - loss: 0.1517 - accuracy: 0.9387 - auc: 0.9686 - val_loss: 0.2472 - val_accuracy: 0.9030 - val_auc: 0.9687\n", "Epoch 91/100\n", - " - 2s - loss: 0.1926 - accuracy: 0.9217 - auc: 0.9547 - val_loss: 0.3292 - val_accuracy: 0.8709 - val_auc: 0.9548\n", + " - 2s - loss: 0.1527 - accuracy: 0.9364 - auc: 0.9688 - val_loss: 0.2475 - val_accuracy: 0.9020 - val_auc: 0.9689\n", "Epoch 92/100\n", - " - 2s - loss: 0.1909 - accuracy: 0.9224 - auc: 0.9549 - val_loss: 0.3262 - val_accuracy: 0.8664 - val_auc: 0.9550\n", + " - 2s - loss: 0.1518 - accuracy: 0.9403 - auc: 0.9690 - val_loss: 0.2488 - val_accuracy: 0.9015 - val_auc: 0.9690\n", "Epoch 93/100\n", - " - 2s - loss: 0.1905 - accuracy: 0.9215 - auc: 0.9551 - val_loss: 0.3251 - val_accuracy: 0.8664 - val_auc: 0.9552\n", + " - 2s - loss: 0.1510 - accuracy: 0.9375 - auc: 0.9691 - val_loss: 0.2485 - val_accuracy: 0.8999 - val_auc: 0.9692\n", "Epoch 94/100\n", - " - 3s - loss: 0.1944 - accuracy: 0.9209 - auc: 0.9553 - val_loss: 0.3241 - val_accuracy: 0.8659 - val_auc: 0.9554\n", + " - 2s - loss: 0.1514 - accuracy: 0.9376 - auc: 0.9693 - val_loss: 0.2524 - val_accuracy: 0.9041 - val_auc: 0.9694\n", "Epoch 95/100\n", - " - 2s - loss: 0.1900 - accuracy: 0.9228 - auc: 0.9555 - val_loss: 0.3303 - val_accuracy: 0.8668 - val_auc: 0.9556\n", + " - 2s - loss: 0.1488 - accuracy: 0.9381 - auc: 0.9695 - val_loss: 0.2525 - val_accuracy: 0.9025 - val_auc: 0.9695\n", "Epoch 96/100\n", - " - 2s - loss: 0.1893 - accuracy: 0.9227 - auc: 0.9557 - val_loss: 0.3387 - val_accuracy: 0.8664 - val_auc: 0.9558\n", + " - 2s - loss: 0.1494 - accuracy: 0.9386 - auc: 0.9696 - val_loss: 0.2476 - val_accuracy: 0.9025 - val_auc: 0.9697\n", "Epoch 97/100\n", - " - 2s - loss: 0.1892 - accuracy: 0.9234 - auc: 0.9559 - val_loss: 0.3270 - val_accuracy: 0.8664 - val_auc: 0.9560\n", + " - 2s - loss: 0.1525 - accuracy: 0.9388 - auc: 0.9698 - val_loss: 0.2483 - val_accuracy: 0.9015 - val_auc: 0.9699\n", "Epoch 98/100\n", - " - 2s - loss: 0.1903 - accuracy: 0.9241 - auc: 0.9561 - val_loss: 0.3312 - val_accuracy: 0.8659 - val_auc: 0.9562\n", + " - 2s - loss: 0.1505 - accuracy: 0.9382 - auc: 0.9699 - val_loss: 0.2514 - val_accuracy: 0.8994 - val_auc: 0.9700\n", "Epoch 99/100\n", - " - 2s - loss: 0.1871 - accuracy: 0.9225 - auc: 0.9563 - val_loss: 0.3377 - val_accuracy: 0.8659 - val_auc: 0.9564\n", + " - 2s - loss: 0.1479 - accuracy: 0.9399 - auc: 0.9701 - val_loss: 0.2569 - val_accuracy: 0.9036 - val_auc: 0.9702\n", "Epoch 100/100\n", - " - 2s - loss: 0.1904 - accuracy: 0.9226 - auc: 0.9564 - val_loss: 0.3282 - val_accuracy: 0.8650 - val_auc: 0.9565\n" + " - 1s - loss: 0.1469 - accuracy: 0.9400 - auc: 0.9702 - val_loss: 0.2522 - val_accuracy: 0.9030 - val_auc: 0.9703\n" ] } ], "source": [ - "history=model.fit(X_train_resample.drop(['proportion','label'],axis=1).values, y_train_resample.values,\n", + "history=model.fit(X_train_resample.drop(['malwareNum','proportion','label'],axis=1).values, y_train_resample.values,\n", "# validation_split=0.2,\n", - " validation_data=(X_test.drop(['proportion','label'],axis=1).values, y_test),\n", + " validation_data=(X_test.drop(['malwareNum','proportion','label'],axis=1).values, y_test),\n", " verbose=2,\n", " epochs=100,batch_size=32)\n", "# history = model.fit(X_train, y_train, validation_split=0.2,epochs=50,batch_size=32, shuffle=True)" @@ -1290,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1325,12 +1333,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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mLNpEfWMjZ43pzQ0nHETPtASen5vPvz5YwZaKWsbnduOPZ49kbJ907vlgBU/OXsezX61vPnZGchzn5fXh3ENyGJWTFvbujcbsjiUIE5Gq6hpIjG3Zq0VVmbO2lFe/2cCbCwspr2kgNlr44ZH9ue74wbz2zQZ+8/piDs3txtlje/PgJ6u57tlvAHdXPm18H+Jjonhi9jreWFBIZko8m8prODS3K/88fywTBuzonnzb6Qdz4WH9+N/CQgZkpTAmJ50+3RItKZgDiiUIE1EafI3888OV3PvhCnp2SWDyiGyOG5rF/PXbeOnrAtZtrSIxNprJI3pyyshs3l2yiQdmrub5Oflsq6rn+KHd+feF40iIjeZ7eX14f9lmKmoamDKiZ/PDYZcfNYB7P1zJmuLt3HHOSI49KCvghX9Q9xRuOOGg9v4KjNlvQtpILSKTgXuAaOAhVb2z1fquwCPAQKAG+KGqLg5m30CskbpznGtZVT3vLdvMW4s2Mj9/G4cN6Mbpo3oxuEcqN7+8kHnrSjl1VDa19Y3MWlFEXYPrIXT4gAzOPSSHyX4Xe4Bv1pdyx4zl9M9M5vazRoRl1E5jwiUsjdQiEg3cB5wIFABzROQNVV3qt9kvgfmqepaIDPW2nxTkvqYTemleAbe+spB6n9I7PZEjBmUye1UxMxZtAiA1PoZ7po1h6pjegHsy98vVJQzpmUqfbkkBjzm2b1deuOrwdjsHYw4UoaxiGg+sVNXVACLyHDAV8L/IDwfuAFDV5SKSKyI9gAFB7BuRUlJSqKys3P2GEUxVeXfpZlYVVXLh+H7NffBnLNrIL15awIQBGdw8eWhzQ2+Dr5Ev15Qwb10pZ43t3SIRpCbENo++aYzZM6FMEL2BfL/3BcBhrbZZAJwNfCoi44F+QE6Q+wIgIlcAVwD07dt3vwRuwuerNSXc8dYyvlm/DYBHPl3Lb04bRlpiLNc/9w3j+nbloUvzWnT3jImOYuKgTCYOygxT1MZEplAmiEDdNVo3eNwJ3CMi84FFwDdAQ5D7uoWqDwIPgmuD2NtgQ+Xmm2+mX79+zRMG/fa3v0VEmDVrFqWlpdTX13P77bczderUMEfaflQVVVo87FVT7+PXry3mpXkF9OgSz51nj2RYdhd+8/pirn9uPiIwrGcXHv7BoUE9C2CM2Xeh/EsrAPr4vc8BCv03UNVy4DIAcd1A1ng/Sbvbd6+8dQtsWrTPh2mh50iY0nb7+bRp07jhhhuaE8QLL7zA22+/zY033kiXLl0oLi5mwoQJnHHGGRHfBdLXqLw8r4C73/+Oep9y3fGDmDa+D1sr67jyyXks2lDGNccN5NrjBpMY557+ffXqiTz1xTo+W1nMn84eGfRkMcaYfRfKBDEHGCwi/YENwDTgAv8NRCQdqFLVOuByYJaqlovIbvc9UIwdO5YtW7ZQWFhIUVERXbt2JTs7mxtvvJFZs2YRFRXFhg0b2Lx5Mz179gx3uPtFTb2PZRvLWVhQRkVNPdFRrlfQK18XsGJLJaNz0oiPjea2N5bw4KzV1NT7qG1o5D+X5HFiq/aC6Cjh0iNyufSI3DCciTGdW8gShKo2iMi1wDu4rqqPqOoSEbnKWz8dGAY8ISI+XAP0j3a17z4HtYs7/VA699xzeemll9i0aRPTpk3j6aefpqioiHnz5hEbG0tubm7AYb4PFOU19cxZU8KX3s/SwrKd5tEF6J+ZzL8vHMeUES4RfrKimLve+47qugb+feEhO00wY4wJr5BW5qrqDGBGq2XT/V7PBgYHu++Batq0afz4xz+muLiYmTNn8sILL9C9e3diY2P56KOPWLduXbhD3Cu+RuWxz9dy17vfUlXnIzZaGJ2Tzg+P7M/YPumMykknMyXejQ7a2EhyXEyLdoejD8ri6IOy9moCG2NM6FlrXzs4+OCDqaiooHfv3mRnZ3PhhRdy+umnk5eXx5gxYxg6dOehkju6pYXl3PrKQhYUlHHckCx+fPQAxvXtuouRQ9seUdSSgzEdkyWIdrJo0Y7G8czMTGbPnh1wu47+DISqKzX88c1lpCfF8q/zx3LaqGy7yBsTgSxBmDZV1/n4bGUxvbsmMrh7CjUNjdz88kLeXLiRE4b14G/fG3VAzKtrjNk7liDMTmrqfTz95Xru/3gVxZVuQpumeQm2VdVxy5ShXHn0ACs1GBPhOkWC6AyNoPsy6KKvUfl6fSkLC8pYsqGMT1YWU1RRy+EDMvjruaMor6ln8YYy8kuqufSIXA4fmLH7gxpjDngRnyASEhLYunUrGRkZEZskVJWtW7eSkJCwx/sWlFbxf88v4Ku1JQB0T41nTJ90fjixf4tE0DT4nTGm84j4BJGTk0NBQQFFRUXhDiWkEhISyMnJCXp7VeX1+YX85rXFKPDHs0Zw4rAedO+y50nGGBOZIj5BxMbG0r9//3CH0WH4GpX3lm7i4U/XMGdtKXn9unL398e0ORS2MabzivgEYRxV5bX5G/j7e9+RX1JNTtdEfnv6cC4+PJfoqMisejPG7BtLEJ1AfkkVv3x1EZ+sKGZUThq/nDKME4f3IMZmTjPG7IIliAhUWdvAV2u2smJzJd9trmTGoo1ERwl/mHowFx7Wr8VwF8YY0xZLEBGmoqaec+7/nO82uyeyu6fGc8LwHvzylKFkpyWGOTrTbhrqoLEB4lq1LalCbQUkdNm346tCVQkkdYMI7R1oLEFEFF+jcv1z81lVtJ17LxjLUYOymqfrNJ3ElmXw9ZOw8Dmo2w4Tb4CJ17tEUTAP3r4ZCuZAvyNh3MUw7Iydk0hbKjbDvEch/0vYuACqtsLgk+C8JyDW7+ajrABKVu9436U3ZAxs+7jVpVC8ApIzIaVn4HgafW4ul/S+Lintqa2rIKU7xKe2XF5ZBPXboWvujmV122HZf93vUd+HeL9RhmvKYM0sd/4bF7rY8y6Dkd+DaO9vra4KyjdAxqADPnnKvjxg1dHk5eXp3Llzwx1G2Nzx1jIemLmaP0w9mIsPzw13OGblB/DGdRAdByk9ILWHuwCmdIfUnu51ag9I7QXJ+/DwoSqs/ghm3QXrPoWoWBgyBSQKlr4GXXIgJ8+9TunhLmbL34TSNRCTAKnZLp7UbOgxHHqOhp4jdlz0q0rgy+nw9RPgq4MeB0P2aEhIh9n3Qf+j4Pzn3LG++Dd88Afw1baMsdsAl0z6HOZ9Xg/Ytt4ls2X/bbl9ajYcdhUc+iN3QV/zCbx9C2xe7Nan9YVeY1ziy8nb9XdTuQU++D1885Q77tR/waAToLER5jwE798G9VWQMdjFV78dFr0MdRVu/8RuMOFq9zkLn4clr0FDtftus4a6775omUtco74Phd+4eH217hwm39nhk4SIzFPVgF+kJYgD2Ny1JSzbVEFtvY/CbTU88tkaLprQl9vPHBnu0A5sdVXuAhC7D8+ElBfC9CMhsStkj4HKzVCxyV2wast23v64X8MxP9/zz1n9sbsAbpjnLoATroYxF7i7cYB1n8Nbv4Cib926o3/mLrqqsO4z+PYtqNjo4irLh9K1gT8nKgZGnw9H3tiyNLDgeXjtKsgZ797nfwFDToXDroSoaO8CuhxWvOvuvBtazXuSkAYjz4NBk9zdeMUmt93qj1wC6jXWvU7rC0f9n7uD37gA1n4C24tcTJNucwmuutSdR+UmV9op/g6+fMB9Zt5l7rhFy2Hsxe48137iksXASbDyfVj7qYt5+JmudBUVA5/cBd+97WKN7wIjznGJoNcYl0BV3bnN/AtsmOtKDYNPcqWPrx+HQ34Ap94NUR23Q4gliAizpaKG2/+3jDcWtJyF9fih3Xng4kOIDWfvpNK1kNw9+GqLjqahFh48Duoq4dI3WlY9BKvRB4+f4e4mr/gYsg5qub6uyiWMyi3u9+KXYOnrcNYDMHqa26ZkDfz3ethevKPk0Wc8jDjbXVRrK+DdX8O8x9zd65H/5xJDTHyAeBpde0RMEAMr1pTBpsXurthX75ZFxcBBkyG9T+B9lrwKL18Osclwyl/cBTTQXXN9tavqabqAxyW548YGaBvbMM+ViPK/cHfiR1zXcrvaCvjk7zD7Xm+B7FxqARh8Mpz8J8gcBPU18NEf4fN/QVwKnPxHGHfJjljrqtzv1v93Ny1yVWaDTmz7/7WqS1BN1V+q8OEfXIIZeR6MOm9HnEldXUkuKRO2rXNVVZsXue+5qYQZl7zj2HHJXgm0Z8vl+4kliAjy4tx8fv+/pdTWN3L1cQO5YHxfEuKiiY+JIi46KrzDiRSvgPsnuru+S/8b3AUpHOqrXXXJ7Ptg8Ilwyt92XCQ++hPM/LO7gCSkwSVvuIuLqrvgb1sHPUa6KpO27go/ugNm3glnTocx5+8+noY6eOpsWP8FXPKau/i9cqWLqd8RLomUF7o7/ZhEGHa6u3Buy3cXzuN+tW+lnf1h81KvDaF7+35uyRr46j8QHeMuok0X0qYLbaDG+M1LITEduvQKfXwz/wof3b777aJiQRtBfbvZUHb8X01Ih+xR0HOUq/Ibcc5eVWdZgogQbywo5KfPfsNh/bvxp7NHMjCrA03R2dgIj53iiv/1VZD3Izjt76H9zE2L4OM7YfId7i66ORYfzH3E3f3nHrmjKqBoOSz7H3z1gKue6Nrf1cNP+n9w1E2weQk8cLT7QzviOnjiTFflcNiVrl56i9+st3Epbv/WSULVxTX6fDjr/uDPpboUHj7JNfDWV7k/+POe2FGCUYXCr11iW/QypGTBmfdD3wl7+eWZdlO8wpXMwCWBqq2uKm17MaT1dhf4rCGuWrOqxJWwGrzSkCrUlu+oOqvbvuO4lZtd6WPLUlcauWnZXoVnCSICzM/fxvcfmM2onDSeuvww4mPanqEtLL76D8z4GUz9t7sQf/5POONfrgi/p1Thu3dcne6wM9xdUmtlG+ChSe6ueuDxcNErO+6ePvk7fPA79zomEfoe5qo2yvLdsgHHwdE/d3fnr/wYFr0IZ/8HvrjfNZxe85VrNN6yHJ6Y6v4we411dde9xro/yI0L3LaBpPaEk/7YsvdLMErXwpNnQf9jXONmW6UCX4NLXB288dO0k4Y6qCjcu+pQLEEc8DaWVXPGvZ8RHxPF69dMJCMlQD3zvij61lW3HPdLd3Frra4Klr0B385wPW6yvSJt5hBXtN+WD/+eADmHwsWvujv4p89xDaQn/wm69fd67PR0vUJ21WC3ZTm8cyus+nDHsuzR7uI88lzX6FtbAY9McRfUsRe6HjZn3u/q4DcuhP8c73rxjLvUNSCu/dTFMPgkV6XkX7XQUOuSwHpvhr9zHnaf06Ris7u7737gTQtrTDAsQRzAiitrufjhr8gvqeLlnxzBkJ6pu99pTz3zfddTI60PXPgidB/mffhK+OI+WPSSK+amZkP1NtfND1y3xu7DXZ3+tvVw9Wzo2s+tqyqBh0+ErStbflZUjGvE7pq7I9HEJMCmhe6ufPVMV31z3K2uqmfJq64r5OZFbrthp7vi9tpP4cIXYMDx8OgUV2q56hN3LlVb4eovgu8vX1UCj53qivnnPmp35qZTsQRxgFpTvJ0fPPoVm8treODiPI45KGv/f8jmJXD/Ea7nyeqPXU+PyXe4bn9LXnW9Ypq6/fWb6OpQt670HhRa4C7sW5a5htK8y1oeu6EOygvcXXil18WzYpOrOy1e4fq113s9R6JiXGLKPdp1Z2zqpgmuymnjfNeXfeGLrpvoaf/Y8XlF38H0iS6xVJfAhS+5ksKeaPS5OmBLDqaTsQRxAPp6fSmXP+7O5aFL8xjXt2toPujlH7uHpm5c7BrAnv6e6+IYlwLjfwwTrnENoqHQ6HPJpqHGPXQUqItma/XVrudKj+Etl8/6K3x4OxxyGZz+j5CEa0wk2lWCsKE2Opiqugbu/3gVD8xaTXZaAo9dNp7+mfu/7zPg6vAXvwwTfuKqY5K6wY/ecT19hkzZuyEN9kRUtKvW2ROxiTsnB4CJN7okM+iE/RObMSa0CUJEJgP3ANHAQ6p6Z6v1acBTQF8vlr+p6qPeurVABeADGtrKcJFCVXlz0Ub+9OYyCstqOHNML35z2vD91yDd2AgLnnU9cA65zPXt//xfrlrl8Gt2bJeQ5hp+DzTRMa59whiz34QsQYhINHAfcCJQAMwRkTdUdanfZtcAS1X1dBHJAr4VkadVtc5bf5yqFocqxo5AVflg2Rbufv87lhSWMzy7C/ecP5ZDc/fj3fv6L+Ctm109PuLGyxk+1Q2zMHpa+zwwZIw54ISyBDEeWKmqqwFE5DlgKuCfIBRIFff4bwpQAjSEMKYOZWtlLT96fC7z87fRt1sSf/veaM4a23v/zvD2/m/h07vdiJrnPAy5R7kEMechN/DaxBv232cZYyJKKBNEbyDf730BcFirbe4F3gAKgVTg+6ra6K1T4F0RUeABVX0w0IeIyBXAFQB9+/YNtEmHdedby1lSWMafzxnJ2eNy9v8YSl8+6JLD2Ithyp93jONy4u/cSJgVm1xVkzHGBBDKUd0C3Qa37jJ1MjAf6AWMAe4VkabBUyaq6jhgCnCNiBwd6ENU9UFVzVPVvKysEPW2CYGv15fy4rwCfnhkf75/aN+9Sw6la91PoJ5o377txv4fcgqcfs/Og3wldQvc2GuMMZ5QliAKAP/hH3NwJQV/lwF3qutru1JE1gBDga9UtRBAVbeIyKu4KqtZIYy33fgaldteX0L31HiuO35wEDvUe8NFb3aP1K/9zD0hXLLKrU9Ic+O5ZA52TywndHFj8vccBec85HoLGWPMHgplgpgDDBaR/sAGYBpwQatt1gOTgE9EpAcwBFgtIslAlKpWeK9PAn4fwljb1Qtz81m0oYx7po0hJX4X/wTbt8KX97uqIv85BGISXFvCYVe6WayaZrda8pp7UAzc+PkXPB+S4YGNMZ1DyBKEqjaIyLXAO7huro+o6hIRucpbPx34A/CYiCzCVUndrKrFIjIAeNUbujoGeEZV3w5VrO1pW1Udf3l7OeNzu3HG6DZ6D9VWuiGn5zzknjQedrqb1CS1pxvOOGto2+PSN9S6J5aTs8I/BLQx5oAW0ucgVHUGMKPVsul+rwtxpYPW+60GRocytnBQVX724kIqaxv43dSDA8/dsPYzeP1qKF3npoY86qY9GyguJr7tiV2MMWYP2JPU7eihT9bw/rLN3Hb6cIZle23xW1e5OQkqNrvJ5Oc95ga8u2yGG47aGGPCxBJEO5m3roQ7317OlBE9+cERuW7hpsVukLkmEuUmaj/hd3s+l4AxxuxnliDaQcn2Oq595ht6pyfy53NH7aha2jjf/T7vCeg50vVAOlDncjbGRBxLECFW19DIT56ax9btdbx81RF0SYjdsbJoOUTHw5BT3VhCxhjTgYTyQblOT1X5zWuL+XJNCX85ZxQjc9JablD0rXt2wZKDMaYDsgQRQg9/uobn5+Zz7XGDOHNs7503KFruuqwaY0wHZAkiRN5evJE/zVjGlBE9+b8TD9p5g7rtbppOSxDGmA7KEkQIvD5/A9c88w1j+qRz13mjiQo0Omvxd+73nk6YY4wx7cQSxH72wtx8bnh+Pnn9uvLEjw4jKa6N9oWib91vK0EYYzooax3dj16fv4FfvLSQowZn8uDFeSTG7WKQvKLlEBUL3fq3X4DGGLMHLEHsJ4Xbqvn1q4sZn9uN/1ySR0LsbkZQLfoWMga5wfaMMaYDsiqm/UBVufnlhfhU+dv3Ru8+OYDXg8naH4wxHZcliP3guTn5fLKimFtPGUbfDO9J6IY6NwR3oMl86qvdRD/W/mCM6cAsQeyjgtIqbv/fUo4YmMGF4/2mPP3qQXjgKHhiKmxZ1nKnrStBG60EYYzp0CxB7KPf/3cpAH8+Z1TL7qzrPofErm4yn/snwlu3gK/Brduy3P22EoQxpgOzBLEPvl5fyrtLN3PVMQPp081vkD1VKPgKDpoCP/0Gxl3iZoab+7BbX7QcJBoyBoYncGOMCYIliL2kqvz5reVkpsTzwyNbdVXdtg62F0FOHiR1g9PudjPCfXg7VGxyCaLbADe5jzHGdFCWIPbSrBXFfLmmhJ9OGkRy63mlC+a63zmHut8icMpf3XSg7/zKdXG19gdjTAdnCWIvNDYqf3l7OX26JTLt0L47b5D/FcQmQ/fhO5ZlDIQjb4TFL7lG6u7D2i9gY4zZC5Yg9sKbizaypLCcm04cQlxMgK+wYA70HrfzMN5H3gBdcwG1BmpjTIdnCWIvPPrZGgZ1T+GM0b3c8wxbV+1YWV8Nmxa69ofWYhNde0Rit8DrjTGmA7GhNvbQhm3VlOYv4+8DvyHq3z9zo7LGpcD1CyA503VrbWyAnPGBDzDwePjFatcuYYwxHZiVIPbQ7Nmf8nLcbYwpfAHScuDYX0J9FXx6t9sg/yv3e1clBEsOxpgDgJUg9kTpWo6bcyVExSLXzNrxHEPpGpjzEBx+rWt/SO8HKd3DG6sxxuyjoEoQIvKyiJwqIntU4hCRySLyrYisFJFbAqxPE5H/isgCEVkiIpcFu2+7K99Iw6NnEO2r4b1DHmj5kNsxN7tqpU/+5rq49mmjeskYYw4gwV7w7wcuAFaIyJ0istsuOCISDdwHTAGGA+eLyPBWm10DLFXV0cCxwF0iEhfkvu3r1Sto3F7EpXU3c/jhR7Vc160/jL0Y5j4KFYU7nn8wxpgDWFAJQlXfV9ULgXHAWuA9EflcRC4TkbYmNBgPrFTV1apaBzwHTG19aCBVRARIAUqAhiD3DQ3VnUdg3bgQ1szi6fhpaO9DdozY6u/on0OUV2NnPZSMMREg6CojEckAfgBcDnwD3INLGO+1sUtvIN/vfYG3zN+9wDCgEFgEXK+qjUHu2xTXFSIyV0TmFhUVBXs6bXvsVHjzppbLvnqAxphE7i6ZwKkjswPvl9YbJvwEkjKgx8h9j8MYY8Is2DaIV4BPgCTgdFU9Q1WfV9XrcHf+AXcLsKz15AgnA/OBXsAY4F4R6RLkvm6h6oOqmqeqeVlZWbs9l93avNgNqrfuc/d+ezEsfJFl3U+lnBROaStBAEy6DX46H2Li9j0OY4wJs2BLEPeq6nBVvUNVN/qvUNW26lMKgD5+73NwJQV/lwGvqLMSWAMMDXLf/U8Vaivc6zdvAl89zHsMfLU8VHciI3untRy1tbWoKEjoEvIwjTGmPQSbIIaJSHrTGxHpKiJX72afOcBgEekvInHANOCNVtusByZ5x+wBDAFWB7nv/le33U3k0/cI2LIUPv8XzHkYHXAc725JZ1zf9JCHYIwxHUWwCeLHqrqt6Y2qlgI/3tUOqtoAXAu8AywDXlDVJSJylYhc5W32B+AIEVkEfADcrKrFbe27B+e1d2rL3e9R34ODJsMHv4OKQjYN+wHb63wc3Dst5CEYY0xHEeyDclEiIqque4/XDXW3Fe2qOgOY0WrZdL/XhcBJwe4bcjVegojvAlP+DKs/hi69mBObByxgRC9LEMaYziPYBPEO8IKITMc1Fl8FvB2yqMKlqf0hvosbdfWCFyChC0vmVxAXHcXgHm21xxtjTOQJNkHcDFwJ/ATXw+hd4KFQBRU2tWXud1ND84BjAFg84wuGZqcSG21DVxljOo+gEoT3bML93k/k8q9i8qgqizeUc8rInmEKyhhjwiOoBCEig4E7cMNeJDQtV9UBIYorPJqrmFKbFxWUVlNWXc/B1v5gjOlkgq0zeRRXemgAjgOeAJ4MVVBh09SLye9ZhiWFrtpphPVgMsZ0MsEmiERV/QAQVV2nqr8Fjg9dWGHSVMUUt6MEsaSwnOgoYWjP1DZ2MsaYyBRsI3WNN9T3ChG5FtgARN6EB7UVLjlE7cibizeUMbh7Cgmx0WEMzBhj2l+wJYgbcOMw/RQ4BLgIuDREMYVPbflOQ2UsLiy39gdjTKe02xKE91Dcear6c6ASN35SZKopa9GDaUt5DUUVtRzcy8ZXMsZ0PrstQaiqDzjEm7MhstVWtOjBtNgaqI0xnViwbRDfAK+LyIvA9qaFqvpKSKIKl9pyN5+DZ/EG12g93EoQxphOKNgE0Q3YSsueSwpEVoKoKXdDbHgWbyhjQGYyKfHBfk3GGBM5gn2SOnLbHfzVVrRog1ixpdK6txpjOq1gn6R+lAAzuqnqD/d7ROHk14upsVHZUFrNScN7hDkoY4wJj2DrTv7n9zoBOIv2mOGtPTXUQUNNcwmiqLKWOl8jOV0TwxyYMcaER7BVTC/7vxeRZ4H3QxJRuPgP9Q0UlFYBkNN1F1OMGmNMBNvb8asHA333ZyBh12qo74LSagD6dLMShDGmcwq2DaKClm0Qm3BzRESOViO5NiWI3ulWgjDGdE7BVjFFfleeVnNBFJRWkZkSR2KcjcFkjOmcgqpiEpGzRCTN7326iJwZsqjCodVQ3/kl1fS29gdjTCcWbBvEbapa1vRGVbcBt4UkonAJ0EhtPZiMMZ1ZsAki0HaR9XixXxVTY6OyYVu1JQhjTKcWbIKYKyJ/F5GBIjJARO4G5oUysHbn14tpS0Ut9T61Lq7GmE4t2ARxHVAHPA+8AFQD14QqqLCorYDoOIiJ93sGwkoQxpjOK9heTNuBW/b04CIyGbgHiAYeUtU7W63/OXChXyzDgCxVLRGRtUAF4AMaVDVvTz9/j9SU+7U/eM9AWAnCGNOJBduL6T0RSfd731VE3tnNPtHAfcAUYDhwvogM999GVf+qqmNUdQxwKzBTVUv8NjnOWx/a5AAtxmGyEoQxxgRfxZTp9VwCQFVL2f2c1OOBlaq6WlXrgOeAqbvY/nzg2SDj2f/8JgsqKK0mMyXe5qE2xnRqwSaIRhFpHlpDRHIJMLprK72BfL/3Bd6ynYhIEjAZ8B/zSYF3RWSeiFwRZJx7r1UVk5UejDGdXbBdVX8FfCoiM733RwO7u2gHmqK0raRyOvBZq+qliapaKCLdgfdEZLmqztrpQ1zyuAKgb999GB6qthy6DQBcFZNNM2qM6eyCKkGo6ttAHvAtrifTTbieTLtSAPTxe59D20OET6NV9ZKqFnq/twCv4qqsAsX2oKrmqWpeVlbWbkLaBa+KacczENZAbYzp3IIdrO9y4HrcRX4+MAGYTcspSFubAwwWkf7ABlwSuCDAsdOAY4CL/JYlA1GqWuG9Pgn4fTCx7jWvimlzRY33DIRVMRljOrdg2yCuBw4F1qnqccBYoGhXO6hqA3At8A6wDHhBVZeIyFUicpXfpmcB73pdaZv0wFVpLQC+At70SjGhodrci6mpi6slCGNMZxdsG0SNqtaICCISr6rLRWTI7nZS1RnAjFbLprd6/xjwWKtlq4HRQca27+oqAYX41OYurn26WRWTMaZzCzZBFHjPQbyGazAuJZKmHPUbh6mgpGkeCCtBGGM6t2CfpD7Le/lbEfkISANCV+XT3vwmCyoorSYr1Z6BMMaYPR6RVVVn7n6rA0zzXBBpFGyzYb6NMQb2fk7qyOJXxbShtNqql4wxBksQTlMJIj6V8poG0pNiwxuPMcZ0AJYgoMV0o1V1DSRa+4MxxliCAJqrmBrjUqmpbyQxLrImyzPGmL1hCQK8XkxCTZRre0iKsxKEMcZYggBXxRSfSlW9G0vQEoQxxliCcLxxmKrrfADWBmGMMViCcLwSRHW9lyCsBGGMMZYggOaB+qq8EoRVMRljjCUIx6tiqqprACAx1noxGWOMJQhoniyo2koQxhjTzBIENFcxWRuEMcbsYAkCvBLEjjYI68VkjDF7MZprRLpipitBLK4DrIrJGGPAEoTTfSgAVXWrAEiyoTaMMcaqmPxVe72Y4mPsazHGGLsS+qmu95EYG01UlIQ7FGOMCTtLEH6q6nzW/mCMMR5LEH6q63zWxdUYYzyWIPxU1fmsi6sxxngsQfiprrcqJmOMaRLSBCEik0XkWxFZKSK3BFj/cxGZ7/0sFhGfiHQLZt9QsComY4zZIWQJQkSigfuAKcBw4HwRGe6/jar+VVXHqOoY4FZgpqqWBLNvKFTVN9gzEMYY4wllCWI8sFJVV6tqHfAcMHUX258PPLuX++4X1gZhjDE7hDJB9Aby/d4XeMt2IiJJwGTg5T3dd3+qsSomY4xpFsoEEehpM21j29OBz1S1ZE/3FZErRGSuiMwtKiraizB3qLJGamOMaRbKBFEA9PF7nwMUtrHtNHZUL+3Rvqr6oKrmqWpeVlbWPoTrVTFZgjDGGCC0CWIOMFhE+otIHC4JvNF6IxFJA44BXt/TffcnX6NS19BobRDGGOMJWZcdVW0QkWuBd4Bo4BFVXSIiV3nrp3ubngW8q6rbd7dvqGIFmicLsiomY4xxQtqnU1VnADNaLZve6v1jwGPB7BtKzfNRWzdXY4wB7EnqZs3zUVsVkzHGAJYgmjVPN2pVTMYYA1iCaNbUBmEJwhhjHEsQHqtiMsaYlixBeJqqmGwsJmOMcSxBeHb0YrKvxBhjwBJEs+rmRmorQRhjDFiCaNb8oJy1QRhjDGAJopl1czXGmJYsQXiq63xECcTH2FdijDFgCaJZ02RBIoFGGjfGmM7HEoSnut5nDdTGGOPHEoSnuq7BRnI1xhg/liA8VXU2m5wxxvizBOGprveRYF1cjTGmmSUIT7WVIIwxpgVLEB6rYjLGmJYsQXisF5MxxrRkCcJTVddAYqx9HcYY08SuiB7XBmElCGOMaWIJwuOqmKwNwhhjmliCAOp9jdT71EZyNcYYP5YgsJFcjTEmEEsQQE29JQhjjGnNEgT+81FbgjDGmCYhTRAiMllEvhWRlSJySxvbHCsi80VkiYjM9Fu+VkQWeevmhjLO5vmoY60XkzHGNAnZFVFEooH7gBOBAmCOiLyhqkv9tkkH/g1MVtX1ItK91WGOU9XiUMXYpNraIIwxZiehLEGMB1aq6mpVrQOeA6a22uYC4BVVXQ+gqltCGE+bmuejtgRhjDHNQpkgegP5fu8LvGX+DgK6isjHIjJPRC7xW6fAu97yK9r6EBG5QkTmisjcoqKivQq0uReTdXM1xphmoax0DzR3pwb4/EOASUAiMFtEvlDV74CJqlroVTu9JyLLVXXWTgdUfRB4ECAvL6/18YNSbY3Uxhizk1CWIAqAPn7vc4DCANu8rarbvbaGWcBoAFUt9H5vAV7FVVmFhD0HYYwxOwtlgpgDDBaR/iISB0wD3mi1zevAUSISIyJJwGHAMhFJFpFUABFJBk4CFocq0KZeTEnWi8kYY5qF7Iqoqg0ici3wDhANPKKqS0TkKm/9dFVdJiJvAwuBRuAhVV0sIgOAV0WkKcZnVPXtUMVqD8oZY8zOQnrLrKozgBmtlk1v9f6vwF9bLVuNV9XUHqrqfMRECXEx9tygMcY0sSsiLkFYDyZjjGnJEgSuF5NVLxljTEuWIHAPylkXV2OMackSBF4Vk80mZ4wxLViCAKrrbT5qY4xpza6KuBKEzUdtjDEtWYLAGqmNMSYQSxBYI7UxxgRiCYKmKiZLEMYY488SBK6KKcEelDPGmBYsQQAnDOvOqJy0cIdhjDEdinXdAf4xbWy4QzDGmA7HShDGGGMCsgRhjDEmIEsQxhhjArIEYYwxJiBLEMYYYwKyBGGMMSYgSxDGGGMCsgRhjDEmIFHVcMew34hIEbBuL3fPBIr3YzgHgs54ztA5z7sznjN0zvPe03Pup6pZgVZEVILYFyIyV1Xzwh1He+qM5wyd87w74zlD5zzv/XnOVsVkjDEmIEsQxhhjArIEscOD4Q4gDDrjOUPnPO/OeM7QOc97v52ztUEYY4wJyEoQxhhjArIEYYwxJqBOnyBEZLKIfCsiK0XklnDHEyoi0kdEPhKRZSKyRESu95Z3E5H3RGSF97truGPd30QkWkS+EZH/ee87wzmni8hLIrLc+zc/PNLPW0Ru9P5vLxaRZ0UkIRLPWUQeEZEtIrLYb1mb5ykit3rXt29F5OQ9+axOnSBEJBq4D5gCDAfOF5Hh4Y0qZBqAm1R1GDABuMY711uAD1R1MPCB9z7SXA8s83vfGc75HuBtVR0KjMadf8Set4j0Bn4K5KnqCCAamEZknvNjwORWywKep/c3Pg042Nvn3951LyidOkEA44GVqrpaVeuA54CpYY4pJFR1o6p+7b2uwF0weuPO93Fvs8eBM8MSYIiISA5wKvCQ3+JIP+cuwNHAwwCqWqeq24jw88ZNoZwoIjFAElBIBJ6zqs4CSlotbus8pwLPqWqtqq4BVuKue0Hp7AmiN5Dv977AWxbRRCQXGAt8CfRQ1Y3gkgjQPYyhhcI/gF8AjX7LIv2cBwBFwKNe1dpDIpJMBJ+3qm4A/gasBzYCZar6LhF8zq20dZ77dI3r7AlCAiyL6H6/IpICvAzcoKrl4Y4nlETkNGCLqs4LdyztLAYYB9yvqmOB7URG1UqbvDr3qUB/oBeQLCIXhTeqDmGfrnGdPUEUAH383ufgiqURSURiccnhaVV9xVu8WUSyvfXZwJZwxRcCE4EzRGQtrvrweBF5isg+Z3D/rwtU9Uvv/Uu4hBHJ530CsEZVi1S1HngFOILIPmd/bZ3nPl3jOnuCmAMMFpH+IhKHa8x5I8wxhYSICK5Oepmq/t1v1RvApd7rS4HX2zu2UFHVW1U1R1Vzcf+2H6rqRUTwOQOo6iYgX0SGeIsmAUuJ7PNeD0wQkSTv//okXDtbJJ+zv7bO8w1gmojEi0h/YDDwVdBHVdVO/QOcAnwHrAJ+Fe54QnieR+KKlguB+d7PKUAGrtfDCu93t3DHGqLzPxb4n/c64s8ZGAPM9f69XwO6Rvp5A78DlgOLgSeB+Eg8Z+BZXDtLPa6E8KNdnSfwK+/69i0wZU8+y4baMMYYE1Bnr2IyxhjTBksQxhhjArIEYYwxJiBLEMYYYwKyBGGMMSYgSxDGdAAicmzTaLPGdBSWIIwxxgRkCcKYPSAiF4nIVyIyX0Qe8OaaqBSRu0TkaxH5QESyvG3HiMgXIrJQRF5tGqNfRAaJyPsissDbZ6B3+BS/ORye9p4INiZsLEEYEyQRGQZ8H5ioqmMAH3AhkAx8rarjgJnAbd4uTwA3q+ooYJHf8qeB+1R1NG68oI3e8rHADbi5SQbgxpIyJmxiwh2AMQeQScAhwBzv5j4RNyhaI/C8t81TwCsikgakq+pMb/njwIsikgr0VtVXAVS1BsA73leqWuC9nw/kAp+G/KyMaYMlCGOCJ8Djqnpri4Uiv2m13a7Gr9lVtVGt32sf9vdpwsyqmIwJ3gfAuSLSHZrnAe6H+zs619vmAuBTVS0DSkXkKG/5xcBMdXNwFIjImd4x4kUkqT1Pwphg2R2KMUFS1aUi8mvgXRGJwo2meQ1uQp6DRWQeUIZrpwA37PJ0LwGsBi7zll8MPCAiv/eO8b12PA1jgmajuRqzj0SkUlVTwh2HMfubVTEZY4wJyEoQxhhjArIShDHGmIAsQRhjjAnIEoQxxpiALEEYY4wJyBKEMcaYgP4/f1H1hUWTqQ4AAAAASUVORK5CYII=\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1371,12 +1379,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "## predict using sensor model\n", - "y_pred_with_sensor = model.predict(X_test.drop(['proportion','label'],axis=1).values)\n", + "y_pred_with_sensor = model.predict(X_test.drop(['malwareNum','proportion','label'],axis=1).values)\n", "y_pred_repack_benign = model.predict_proba(repackaged_benign_test_X)\n", "covid_test_X.fillna(0, inplace = True)\n", "# covid_y_pred_w_sensor = model.predict_proba(covid_test_X)" @@ -1391,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1402,7 +1410,7 @@ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "dense_1 (Dense) (None, 48) 15600 \n", + "dense_1 (Dense) (None, 48) 15504 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 48) 0 \n", "_________________________________________________________________\n", @@ -1412,8 +1420,8 @@ "_________________________________________________________________\n", "dense_3 (Dense) (None, 1) 65 \n", "=================================================================\n", - "Total params: 18,801\n", - "Trainable params: 18,801\n", + "Total params: 18,705\n", + "Trainable params: 18,705\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -1423,7 +1431,7 @@ "tf.keras.backend.clear_session()\n", "model_wo = keras.Sequential()\n", "model_wo = Sequential()\n", - "model_wo.add(Dense(48, input_dim=X_train_wo_sensor_resample.shape[1]-2, activation='relu'))\n", + "model_wo.add(Dense(48, input_dim=X_train_wo_sensor_resample.drop(['malwareNum','proportion','label'],axis=1).shape[1], activation='relu'))\n", "model_wo.add(Dropout(0.4))\n", "model_wo.add(Dense(64, activation='relu'))\n", "model_wo.add(Dropout(0.4))\n", @@ -1437,232 +1445,237 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, + "execution_count": 42, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train on 13162 samples, validate on 2230 samples\n", + "Train on 10848 samples, validate on 1908 samples\n", "Epoch 1/100\n", - " - 5s - loss: 0.4577 - accuracy: 0.7815 - auc: 0.7948 - val_loss: 0.3078 - val_accuracy: 0.8525 - val_auc: 0.8724\n", + " - 3s - loss: 0.4458 - accuracy: 0.7771 - auc: 0.7864 - val_loss: 0.3259 - val_accuracy: 0.8664 - val_auc: 0.8783\n", "Epoch 2/100\n", - " - 2s - loss: 0.3354 - accuracy: 0.8525 - auc: 0.8932 - val_loss: 0.3167 - val_accuracy: 0.8386 - val_auc: 0.9055\n", + " - 2s - loss: 0.3112 - accuracy: 0.8625 - auc: 0.8999 - val_loss: 0.3154 - val_accuracy: 0.8611 - val_auc: 0.9148\n", "Epoch 3/100\n", - " - 2s - loss: 0.2936 - accuracy: 0.8721 - auc: 0.9133 - val_loss: 0.2802 - val_accuracy: 0.8704 - val_auc: 0.9203\n", + " - 1s - loss: 0.2791 - accuracy: 0.8811 - auc: 0.9224 - val_loss: 0.2824 - val_accuracy: 0.8742 - val_auc: 0.9289\n", "Epoch 4/100\n", - " - 3s - loss: 0.2766 - accuracy: 0.8821 - auc: 0.9247 - val_loss: 0.3268 - val_accuracy: 0.8547 - val_auc: 0.9286\n", + " - 2s - loss: 0.2574 - accuracy: 0.8918 - auc: 0.9335 - val_loss: 0.2737 - val_accuracy: 0.8768 - val_auc: 0.9375\n", "Epoch 5/100\n", - " - 3s - loss: 0.2647 - accuracy: 0.8873 - auc: 0.9316 - val_loss: 0.3026 - val_accuracy: 0.8561 - val_auc: 0.9339\n", + " - 2s - loss: 0.2405 - accuracy: 0.8977 - auc: 0.9403 - val_loss: 0.2766 - val_accuracy: 0.8700 - val_auc: 0.9432\n", "Epoch 6/100\n", - " - 2s - loss: 0.2564 - accuracy: 0.8933 - auc: 0.9360 - val_loss: 0.2985 - val_accuracy: 0.8605 - val_auc: 0.9379\n", + " - 2s - loss: 0.2343 - accuracy: 0.9023 - auc: 0.9451 - val_loss: 0.2708 - val_accuracy: 0.8810 - val_auc: 0.9472\n", "Epoch 7/100\n", - " - 3s - loss: 0.2492 - accuracy: 0.8923 - auc: 0.9395 - val_loss: 0.2912 - val_accuracy: 0.8637 - val_auc: 0.9410\n", + " - 2s - loss: 0.2246 - accuracy: 0.9075 - auc: 0.9488 - val_loss: 0.2712 - val_accuracy: 0.8747 - val_auc: 0.9503\n", "Epoch 8/100\n", - " - 3s - loss: 0.2434 - accuracy: 0.8959 - auc: 0.9423 - val_loss: 0.3246 - val_accuracy: 0.8529 - val_auc: 0.9434\n", + " - 2s - loss: 0.2157 - accuracy: 0.9129 - auc: 0.9517 - val_loss: 0.2808 - val_accuracy: 0.8737 - val_auc: 0.9529\n", "Epoch 9/100\n", - " - 3s - loss: 0.2372 - accuracy: 0.9001 - auc: 0.9444 - val_loss: 0.3010 - val_accuracy: 0.8677 - val_auc: 0.9454\n", + " - 2s - loss: 0.2080 - accuracy: 0.9129 - auc: 0.9541 - val_loss: 0.2637 - val_accuracy: 0.8816 - val_auc: 0.9551\n", "Epoch 10/100\n", - " - 2s - loss: 0.2345 - accuracy: 0.9019 - auc: 0.9464 - val_loss: 0.2914 - val_accuracy: 0.8664 - val_auc: 0.9473\n", + " - 2s - loss: 0.2090 - accuracy: 0.9126 - auc: 0.9560 - val_loss: 0.2877 - val_accuracy: 0.8758 - val_auc: 0.9568\n", "Epoch 11/100\n", - " - 2s - loss: 0.2319 - accuracy: 0.9001 - auc: 0.9481 - val_loss: 0.3529 - val_accuracy: 0.8439 - val_auc: 0.9487\n", + " - 2s - loss: 0.2030 - accuracy: 0.9120 - auc: 0.9575 - val_loss: 0.2860 - val_accuracy: 0.8800 - val_auc: 0.9582\n", "Epoch 12/100\n", - " - 2s - loss: 0.2263 - accuracy: 0.9046 - auc: 0.9493 - val_loss: 0.3306 - val_accuracy: 0.8574 - val_auc: 0.9499\n", + " - 2s - loss: 0.1925 - accuracy: 0.9194 - auc: 0.9589 - val_loss: 0.2956 - val_accuracy: 0.8810 - val_auc: 0.9596\n", "Epoch 13/100\n", - " - 2s - loss: 0.2247 - accuracy: 0.9050 - auc: 0.9505 - val_loss: 0.2995 - val_accuracy: 0.8704 - val_auc: 0.9511\n", + " - 2s - loss: 0.1930 - accuracy: 0.9185 - auc: 0.9603 - val_loss: 0.2923 - val_accuracy: 0.8816 - val_auc: 0.9608\n", "Epoch 14/100\n", - " - 2s - loss: 0.2225 - accuracy: 0.9050 - auc: 0.9516 - val_loss: 0.3209 - val_accuracy: 0.8619 - val_auc: 0.9521\n", + " - 2s - loss: 0.1895 - accuracy: 0.9202 - auc: 0.9613 - val_loss: 0.2782 - val_accuracy: 0.8894 - val_auc: 0.9618\n", "Epoch 15/100\n", - " - 2s - loss: 0.2213 - accuracy: 0.9046 - auc: 0.9526 - val_loss: 0.3239 - val_accuracy: 0.8605 - val_auc: 0.9530\n", + " - 2s - loss: 0.1872 - accuracy: 0.9229 - auc: 0.9624 - val_loss: 0.2903 - val_accuracy: 0.8857 - val_auc: 0.9628\n", "Epoch 16/100\n", - " - 2s - loss: 0.2172 - accuracy: 0.9068 - auc: 0.9534 - val_loss: 0.3211 - val_accuracy: 0.8619 - val_auc: 0.9538\n", + " - 2s - loss: 0.1867 - accuracy: 0.9203 - auc: 0.9632 - val_loss: 0.2877 - val_accuracy: 0.8842 - val_auc: 0.9636\n", "Epoch 17/100\n", - " - 2s - loss: 0.2190 - accuracy: 0.9068 - auc: 0.9542 - val_loss: 0.3144 - val_accuracy: 0.8664 - val_auc: 0.9546\n", + " - 2s - loss: 0.1835 - accuracy: 0.9221 - auc: 0.9640 - val_loss: 0.2858 - val_accuracy: 0.8905 - val_auc: 0.9644\n", "Epoch 18/100\n", - " - 2s - loss: 0.2095 - accuracy: 0.9091 - auc: 0.9550 - val_loss: 0.3202 - val_accuracy: 0.8682 - val_auc: 0.9554\n", + " - 2s - loss: 0.1816 - accuracy: 0.9239 - auc: 0.9648 - val_loss: 0.3151 - val_accuracy: 0.8747 - val_auc: 0.9651\n", "Epoch 19/100\n", - " - 2s - loss: 0.2135 - accuracy: 0.9076 - auc: 0.9557 - val_loss: 0.3294 - val_accuracy: 0.8605 - val_auc: 0.9560\n", + " - 2s - loss: 0.1790 - accuracy: 0.9233 - auc: 0.9654 - val_loss: 0.2872 - val_accuracy: 0.8947 - val_auc: 0.9657\n", "Epoch 20/100\n", - " - 2s - loss: 0.2107 - accuracy: 0.9081 - auc: 0.9564 - val_loss: 0.3277 - val_accuracy: 0.8726 - val_auc: 0.9566\n", + " - 2s - loss: 0.1789 - accuracy: 0.9251 - auc: 0.9660 - val_loss: 0.3034 - val_accuracy: 0.8842 - val_auc: 0.9663\n", "Epoch 21/100\n", - " - 2s - loss: 0.2092 - accuracy: 0.9108 - auc: 0.9569 - val_loss: 0.3147 - val_accuracy: 0.8753 - val_auc: 0.9572\n", + " - 2s - loss: 0.1721 - accuracy: 0.9269 - auc: 0.9666 - val_loss: 0.3260 - val_accuracy: 0.8774 - val_auc: 0.9668\n", "Epoch 22/100\n", - " - 2s - loss: 0.2060 - accuracy: 0.9096 - auc: 0.9575 - val_loss: 0.3303 - val_accuracy: 0.8628 - val_auc: 0.9578\n", + " - 2s - loss: 0.1728 - accuracy: 0.9248 - auc: 0.9671 - val_loss: 0.3010 - val_accuracy: 0.8873 - val_auc: 0.9673\n", "Epoch 23/100\n", - " - 2s - loss: 0.2081 - accuracy: 0.9102 - auc: 0.9581 - val_loss: 0.3245 - val_accuracy: 0.8735 - val_auc: 0.9583\n", + " - 2s - loss: 0.1736 - accuracy: 0.9254 - auc: 0.9676 - val_loss: 0.3114 - val_accuracy: 0.8847 - val_auc: 0.9678\n", "Epoch 24/100\n", - " - 2s - loss: 0.2005 - accuracy: 0.9094 - auc: 0.9586 - val_loss: 0.3393 - val_accuracy: 0.8632 - val_auc: 0.9588\n", + " - 2s - loss: 0.1721 - accuracy: 0.9283 - auc: 0.9680 - val_loss: 0.3084 - val_accuracy: 0.8857 - val_auc: 0.9683\n", "Epoch 25/100\n", - " - 2s - loss: 0.2032 - accuracy: 0.9121 - auc: 0.9590 - val_loss: 0.3468 - val_accuracy: 0.8646 - val_auc: 0.9592\n", + " - 2s - loss: 0.1658 - accuracy: 0.9294 - auc: 0.9685 - val_loss: 0.3103 - val_accuracy: 0.8884 - val_auc: 0.9687\n", "Epoch 26/100\n", - " - 2s - loss: 0.2036 - accuracy: 0.9095 - auc: 0.9594 - val_loss: 0.3541 - val_accuracy: 0.8614 - val_auc: 0.9596\n", + " - 2s - loss: 0.1672 - accuracy: 0.9274 - auc: 0.9689 - val_loss: 0.3233 - val_accuracy: 0.8905 - val_auc: 0.9691\n", "Epoch 27/100\n", - " - 2s - loss: 0.2012 - accuracy: 0.9126 - auc: 0.9598 - val_loss: 0.3404 - val_accuracy: 0.8659 - val_auc: 0.9600\n", + " - 2s - loss: 0.1633 - accuracy: 0.9294 - auc: 0.9693 - val_loss: 0.3225 - val_accuracy: 0.8878 - val_auc: 0.9695\n", "Epoch 28/100\n", - " - 2s - loss: 0.1966 - accuracy: 0.9148 - auc: 0.9602 - val_loss: 0.3470 - val_accuracy: 0.8664 - val_auc: 0.9604\n", + " - 2s - loss: 0.1636 - accuracy: 0.9290 - auc: 0.9697 - val_loss: 0.3360 - val_accuracy: 0.8899 - val_auc: 0.9699\n", "Epoch 29/100\n", - " - 2s - loss: 0.2023 - accuracy: 0.9116 - auc: 0.9606 - val_loss: 0.3410 - val_accuracy: 0.8655 - val_auc: 0.9607\n", + " - 2s - loss: 0.1683 - accuracy: 0.9263 - auc: 0.9700 - val_loss: 0.3296 - val_accuracy: 0.8926 - val_auc: 0.9702\n", "Epoch 30/100\n", - " - 2s - loss: 0.1980 - accuracy: 0.9138 - auc: 0.9609 - val_loss: 0.3335 - val_accuracy: 0.8686 - val_auc: 0.9611\n", + " - 2s - loss: 0.1648 - accuracy: 0.9285 - auc: 0.9703 - val_loss: 0.3079 - val_accuracy: 0.8878 - val_auc: 0.9705\n", "Epoch 31/100\n", - " - 2s - loss: 0.1962 - accuracy: 0.9152 - auc: 0.9613 - val_loss: 0.3439 - val_accuracy: 0.8655 - val_auc: 0.9614\n", + " - 2s - loss: 0.1596 - accuracy: 0.9306 - auc: 0.9706 - val_loss: 0.3391 - val_accuracy: 0.8884 - val_auc: 0.9708\n", "Epoch 32/100\n", - " - 2s - loss: 0.1978 - accuracy: 0.9145 - auc: 0.9616 - val_loss: 0.3591 - val_accuracy: 0.8574 - val_auc: 0.9617\n", + " - 2s - loss: 0.1665 - accuracy: 0.9267 - auc: 0.9709 - val_loss: 0.3428 - val_accuracy: 0.8878 - val_auc: 0.9710\n", "Epoch 33/100\n", - " - 2s - loss: 0.1968 - accuracy: 0.9128 - auc: 0.9618 - val_loss: 0.3519 - val_accuracy: 0.8646 - val_auc: 0.9620\n", + " - 2s - loss: 0.1615 - accuracy: 0.9298 - auc: 0.9712 - val_loss: 0.3385 - val_accuracy: 0.8847 - val_auc: 0.9713\n", "Epoch 34/100\n", - " - 2s - loss: 0.1951 - accuracy: 0.9147 - auc: 0.9621 - val_loss: 0.3540 - val_accuracy: 0.8655 - val_auc: 0.9623\n", + " - 2s - loss: 0.1605 - accuracy: 0.9296 - auc: 0.9714 - val_loss: 0.3416 - val_accuracy: 0.8884 - val_auc: 0.9715\n", "Epoch 35/100\n", - " - 2s - loss: 0.1972 - accuracy: 0.9122 - auc: 0.9624 - val_loss: 0.3524 - val_accuracy: 0.8691 - val_auc: 0.9625\n", + " - 2s - loss: 0.1514 - accuracy: 0.9335 - auc: 0.9717 - val_loss: 0.3589 - val_accuracy: 0.8863 - val_auc: 0.9718\n", "Epoch 36/100\n", - " - 2s - loss: 0.1928 - accuracy: 0.9129 - auc: 0.9627 - val_loss: 0.3664 - val_accuracy: 0.8682 - val_auc: 0.9628\n", + " - 2s - loss: 0.1597 - accuracy: 0.9292 - auc: 0.9719 - val_loss: 0.3412 - val_accuracy: 0.8889 - val_auc: 0.9721\n", "Epoch 37/100\n", - " - 2s - loss: 0.1956 - accuracy: 0.9118 - auc: 0.9629 - val_loss: 0.3737 - val_accuracy: 0.8744 - val_auc: 0.9630\n", + " - 2s - loss: 0.1597 - accuracy: 0.9294 - auc: 0.9722 - val_loss: 0.3632 - val_accuracy: 0.8889 - val_auc: 0.9723\n", "Epoch 38/100\n", - " - 2s - loss: 0.1907 - accuracy: 0.9132 - auc: 0.9631 - val_loss: 0.3668 - val_accuracy: 0.8668 - val_auc: 0.9632\n", + " - 2s - loss: 0.1562 - accuracy: 0.9278 - auc: 0.9724 - val_loss: 0.3584 - val_accuracy: 0.8962 - val_auc: 0.9725\n", "Epoch 39/100\n", - " - 2s - loss: 0.1936 - accuracy: 0.9135 - auc: 0.9633 - val_loss: 0.3628 - val_accuracy: 0.8758 - val_auc: 0.9634\n", + " - 2s - loss: 0.1564 - accuracy: 0.9298 - auc: 0.9726 - val_loss: 0.3560 - val_accuracy: 0.8899 - val_auc: 0.9727\n", "Epoch 40/100\n", - " - 2s - loss: 0.1920 - accuracy: 0.9124 - auc: 0.9636 - val_loss: 0.3586 - val_accuracy: 0.8659 - val_auc: 0.9637\n", + " - 2s - loss: 0.1572 - accuracy: 0.9288 - auc: 0.9728 - val_loss: 0.3695 - val_accuracy: 0.8842 - val_auc: 0.9729\n", "Epoch 41/100\n", - " - 2s - loss: 0.1880 - accuracy: 0.9160 - auc: 0.9638 - val_loss: 0.3769 - val_accuracy: 0.8744 - val_auc: 0.9639\n", + " - 2s - loss: 0.1576 - accuracy: 0.9288 - auc: 0.9730 - val_loss: 0.3756 - val_accuracy: 0.8878 - val_auc: 0.9730\n", "Epoch 42/100\n", - " - 2s - loss: 0.1887 - accuracy: 0.9154 - auc: 0.9640 - val_loss: 0.3735 - val_accuracy: 0.8700 - val_auc: 0.9641\n", + " - 2s - loss: 0.1563 - accuracy: 0.9298 - auc: 0.9731 - val_loss: 0.3643 - val_accuracy: 0.8852 - val_auc: 0.9732\n", "Epoch 43/100\n", - " - 2s - loss: 0.1896 - accuracy: 0.9155 - auc: 0.9642 - val_loss: 0.3764 - val_accuracy: 0.8713 - val_auc: 0.9643\n", + " - 2s - loss: 0.1493 - accuracy: 0.9327 - auc: 0.9733 - val_loss: 0.3826 - val_accuracy: 0.8821 - val_auc: 0.9734\n", "Epoch 44/100\n", - " - 2s - loss: 0.1857 - accuracy: 0.9184 - auc: 0.9644 - val_loss: 0.3790 - val_accuracy: 0.8731 - val_auc: 0.9645\n", + " - 2s - loss: 0.1480 - accuracy: 0.9320 - auc: 0.9735 - val_loss: 0.3962 - val_accuracy: 0.8899 - val_auc: 0.9736\n", "Epoch 45/100\n", - " - 2s - loss: 0.1867 - accuracy: 0.9148 - auc: 0.9646 - val_loss: 0.4001 - val_accuracy: 0.8655 - val_auc: 0.9647\n", + " - 2s - loss: 0.1523 - accuracy: 0.9308 - auc: 0.9737 - val_loss: 0.3826 - val_accuracy: 0.8931 - val_auc: 0.9737\n", "Epoch 46/100\n", - " - 2s - loss: 0.1871 - accuracy: 0.9164 - auc: 0.9648 - val_loss: 0.3898 - val_accuracy: 0.8686 - val_auc: 0.9648\n", + " - 2s - loss: 0.1550 - accuracy: 0.9319 - auc: 0.9738 - val_loss: 0.4065 - val_accuracy: 0.8784 - val_auc: 0.9739\n", "Epoch 47/100\n", - " - 2s - loss: 0.1866 - accuracy: 0.9180 - auc: 0.9649 - val_loss: 0.3781 - val_accuracy: 0.8668 - val_auc: 0.9650\n", + " - 2s - loss: 0.1499 - accuracy: 0.9332 - auc: 0.9739 - val_loss: 0.4217 - val_accuracy: 0.8899 - val_auc: 0.9740\n", "Epoch 48/100\n", - " - 2s - loss: 0.1869 - accuracy: 0.9151 - auc: 0.9651 - val_loss: 0.4157 - val_accuracy: 0.8668 - val_auc: 0.9652\n", + " - 2s - loss: 0.1491 - accuracy: 0.9320 - auc: 0.9741 - val_loss: 0.3908 - val_accuracy: 0.8926 - val_auc: 0.9742\n", "Epoch 49/100\n", - " - 2s - loss: 0.1844 - accuracy: 0.9201 - auc: 0.9652 - val_loss: 0.3963 - val_accuracy: 0.8619 - val_auc: 0.9653\n", + " - 2s - loss: 0.1461 - accuracy: 0.9318 - auc: 0.9743 - val_loss: 0.4204 - val_accuracy: 0.8905 - val_auc: 0.9743\n", "Epoch 50/100\n", - " - 2s - loss: 0.1813 - accuracy: 0.9178 - auc: 0.9654 - val_loss: 0.4167 - val_accuracy: 0.8655 - val_auc: 0.9655\n", + " - 2s - loss: 0.1514 - accuracy: 0.9294 - auc: 0.9744 - val_loss: 0.4178 - val_accuracy: 0.8889 - val_auc: 0.9744\n", "Epoch 51/100\n", - " - 2s - loss: 0.1831 - accuracy: 0.9205 - auc: 0.9656 - val_loss: 0.3999 - val_accuracy: 0.8686 - val_auc: 0.9656\n", + " - 2s - loss: 0.1519 - accuracy: 0.9343 - auc: 0.9745 - val_loss: 0.3970 - val_accuracy: 0.8915 - val_auc: 0.9746\n", "Epoch 52/100\n", - " - 2s - loss: 0.1811 - accuracy: 0.9180 - auc: 0.9657 - val_loss: 0.4207 - val_accuracy: 0.8704 - val_auc: 0.9658\n", + " - 2s - loss: 0.1448 - accuracy: 0.9332 - auc: 0.9746 - val_loss: 0.4135 - val_accuracy: 0.8894 - val_auc: 0.9747\n", "Epoch 53/100\n", - " - 2s - loss: 0.1832 - accuracy: 0.9179 - auc: 0.9659 - val_loss: 0.4232 - val_accuracy: 0.8565 - val_auc: 0.9659\n", + " - 2s - loss: 0.1485 - accuracy: 0.9329 - auc: 0.9748 - val_loss: 0.4029 - val_accuracy: 0.8873 - val_auc: 0.9748\n", "Epoch 54/100\n", - " - 2s - loss: 0.1859 - accuracy: 0.9176 - auc: 0.9660 - val_loss: 0.4039 - val_accuracy: 0.8655 - val_auc: 0.9660\n", + " - 2s - loss: 0.1450 - accuracy: 0.9346 - auc: 0.9749 - val_loss: 0.4273 - val_accuracy: 0.8905 - val_auc: 0.9750\n", "Epoch 55/100\n", - " - 2s - loss: 0.1834 - accuracy: 0.9214 - auc: 0.9661 - val_loss: 0.4342 - val_accuracy: 0.8623 - val_auc: 0.9662\n", + " - 2s - loss: 0.1466 - accuracy: 0.9313 - auc: 0.9750 - val_loss: 0.4343 - val_accuracy: 0.8920 - val_auc: 0.9751\n", "Epoch 56/100\n", - " - 2s - loss: 0.1810 - accuracy: 0.9183 - auc: 0.9663 - val_loss: 0.4308 - val_accuracy: 0.8596 - val_auc: 0.9663\n", + " - 2s - loss: 0.1457 - accuracy: 0.9322 - auc: 0.9751 - val_loss: 0.4120 - val_accuracy: 0.8905 - val_auc: 0.9752\n", "Epoch 57/100\n", - " - 2s - loss: 0.1800 - accuracy: 0.9198 - auc: 0.9664 - val_loss: 0.4271 - val_accuracy: 0.8722 - val_auc: 0.9664\n", + " - 2s - loss: 0.1474 - accuracy: 0.9320 - auc: 0.9752 - val_loss: 0.4314 - val_accuracy: 0.8905 - val_auc: 0.9753\n", "Epoch 58/100\n", - " - 2s - loss: 0.1818 - accuracy: 0.9186 - auc: 0.9665 - val_loss: 0.4489 - val_accuracy: 0.8700 - val_auc: 0.9666\n", + " - 2s - loss: 0.1490 - accuracy: 0.9306 - auc: 0.9753 - val_loss: 0.4135 - val_accuracy: 0.8931 - val_auc: 0.9754\n", "Epoch 59/100\n", - " - 2s - loss: 0.1826 - accuracy: 0.9160 - auc: 0.9666 - val_loss: 0.4270 - val_accuracy: 0.8722 - val_auc: 0.9667\n", + " - 2s - loss: 0.1460 - accuracy: 0.9306 - auc: 0.9754 - val_loss: 0.4231 - val_accuracy: 0.8905 - val_auc: 0.9755\n", "Epoch 60/100\n", - " - 2s - loss: 0.1796 - accuracy: 0.9181 - auc: 0.9667 - val_loss: 0.4211 - val_accuracy: 0.8673 - val_auc: 0.9668\n", + " - 2s - loss: 0.1464 - accuracy: 0.9345 - auc: 0.9755 - val_loss: 0.4168 - val_accuracy: 0.8910 - val_auc: 0.9756\n", "Epoch 61/100\n", - " - 2s - loss: 0.1801 - accuracy: 0.9186 - auc: 0.9669 - val_loss: 0.4325 - val_accuracy: 0.8637 - val_auc: 0.9669\n", + " - 2s - loss: 0.1464 - accuracy: 0.9346 - auc: 0.9756 - val_loss: 0.4375 - val_accuracy: 0.8905 - val_auc: 0.9757\n", "Epoch 62/100\n", - " - 2s - loss: 0.1814 - accuracy: 0.9192 - auc: 0.9669 - val_loss: 0.4259 - val_accuracy: 0.8700 - val_auc: 0.9670\n", + " - 2s - loss: 0.1457 - accuracy: 0.9320 - auc: 0.9758 - val_loss: 0.4276 - val_accuracy: 0.8905 - val_auc: 0.9758\n", "Epoch 63/100\n", - " - 2s - loss: 0.1795 - accuracy: 0.9192 - auc: 0.9671 - val_loss: 0.4131 - val_accuracy: 0.8709 - val_auc: 0.9671\n", + " - 2s - loss: 0.1432 - accuracy: 0.9348 - auc: 0.9758 - val_loss: 0.4433 - val_accuracy: 0.8915 - val_auc: 0.9759\n", "Epoch 64/100\n", - " - 2s - loss: 0.1803 - accuracy: 0.9200 - auc: 0.9672 - val_loss: 0.4356 - val_accuracy: 0.8713 - val_auc: 0.9672\n", + " - 2s - loss: 0.1427 - accuracy: 0.9347 - auc: 0.9759 - val_loss: 0.4374 - val_accuracy: 0.8952 - val_auc: 0.9760\n", "Epoch 65/100\n", - " - 2s - loss: 0.1770 - accuracy: 0.9167 - auc: 0.9673 - val_loss: 0.4479 - val_accuracy: 0.8700 - val_auc: 0.9673\n", + " - 2s - loss: 0.1440 - accuracy: 0.9346 - auc: 0.9760 - val_loss: 0.4399 - val_accuracy: 0.8978 - val_auc: 0.9761\n", "Epoch 66/100\n", - " - 2s - loss: 0.1747 - accuracy: 0.9198 - auc: 0.9674 - val_loss: 0.4576 - val_accuracy: 0.8695 - val_auc: 0.9674\n", + " - 2s - loss: 0.1448 - accuracy: 0.9338 - auc: 0.9761 - val_loss: 0.4254 - val_accuracy: 0.8816 - val_auc: 0.9761\n", "Epoch 67/100\n", - " - 2s - loss: 0.1762 - accuracy: 0.9189 - auc: 0.9675 - val_loss: 0.4516 - val_accuracy: 0.8709 - val_auc: 0.9675\n", + " - 2s - loss: 0.1423 - accuracy: 0.9342 - auc: 0.9762 - val_loss: 0.4541 - val_accuracy: 0.8952 - val_auc: 0.9762\n", "Epoch 68/100\n", - " - 3s - loss: 0.1788 - accuracy: 0.9199 - auc: 0.9676 - val_loss: 0.4403 - val_accuracy: 0.8650 - val_auc: 0.9676\n", + " - 2s - loss: 0.1435 - accuracy: 0.9346 - auc: 0.9763 - val_loss: 0.4525 - val_accuracy: 0.8910 - val_auc: 0.9763\n", "Epoch 69/100\n", - " - 3s - loss: 0.1760 - accuracy: 0.9207 - auc: 0.9677 - val_loss: 0.4781 - val_accuracy: 0.8655 - val_auc: 0.9677\n", + " - 2s - loss: 0.1465 - accuracy: 0.9320 - auc: 0.9763 - val_loss: 0.4446 - val_accuracy: 0.8920 - val_auc: 0.9764\n", "Epoch 70/100\n", - " - 2s - loss: 0.1773 - accuracy: 0.9207 - auc: 0.9677 - val_loss: 0.4484 - val_accuracy: 0.8655 - val_auc: 0.9678\n", + " - 2s - loss: 0.1383 - accuracy: 0.9350 - auc: 0.9764 - val_loss: 0.4729 - val_accuracy: 0.8920 - val_auc: 0.9765\n", "Epoch 71/100\n", - " - 2s - loss: 0.1779 - accuracy: 0.9203 - auc: 0.9678 - val_loss: 0.4128 - val_accuracy: 0.8713 - val_auc: 0.9679\n", + " - 2s - loss: 0.1377 - accuracy: 0.9347 - auc: 0.9765 - val_loss: 0.4733 - val_accuracy: 0.8957 - val_auc: 0.9766\n", "Epoch 72/100\n", - " - 2s - loss: 0.1741 - accuracy: 0.9214 - auc: 0.9679 - val_loss: 0.4663 - val_accuracy: 0.8655 - val_auc: 0.9680\n", + " - 2s - loss: 0.1416 - accuracy: 0.9334 - auc: 0.9766 - val_loss: 0.4516 - val_accuracy: 0.8931 - val_auc: 0.9766\n", "Epoch 73/100\n", - " - 2s - loss: 0.1766 - accuracy: 0.9214 - auc: 0.9680 - val_loss: 0.4110 - val_accuracy: 0.8717 - val_auc: 0.9680\n", + " - 2s - loss: 0.1385 - accuracy: 0.9343 - auc: 0.9767 - val_loss: 0.5185 - val_accuracy: 0.8899 - val_auc: 0.9767\n", "Epoch 74/100\n", - " - 2s - loss: 0.1724 - accuracy: 0.9215 - auc: 0.9681 - val_loss: 0.4861 - val_accuracy: 0.8722 - val_auc: 0.9681\n", + " - 2s - loss: 0.1468 - accuracy: 0.9345 - auc: 0.9767 - val_loss: 0.4422 - val_accuracy: 0.8910 - val_auc: 0.9768\n", "Epoch 75/100\n", - " - 2s - loss: 0.1777 - accuracy: 0.9199 - auc: 0.9682 - val_loss: 0.4728 - val_accuracy: 0.8762 - val_auc: 0.9682\n", + " - 2s - loss: 0.1402 - accuracy: 0.9363 - auc: 0.9768 - val_loss: 0.4701 - val_accuracy: 0.8884 - val_auc: 0.9768\n", "Epoch 76/100\n", - " - 2s - loss: 0.1700 - accuracy: 0.9233 - auc: 0.9683 - val_loss: 0.4779 - val_accuracy: 0.8749 - val_auc: 0.9683\n", + " - 2s - loss: 0.1395 - accuracy: 0.9357 - auc: 0.9769 - val_loss: 0.4797 - val_accuracy: 0.8905 - val_auc: 0.9769\n", "Epoch 77/100\n", - " - 2s - loss: 0.1751 - accuracy: 0.9188 - auc: 0.9684 - val_loss: 0.4693 - val_accuracy: 0.8668 - val_auc: 0.9684\n", + " - 2s - loss: 0.1444 - accuracy: 0.9311 - auc: 0.9769 - val_loss: 0.4863 - val_accuracy: 0.8941 - val_auc: 0.9769\n", "Epoch 78/100\n", - " - 2s - loss: 0.1745 - accuracy: 0.9187 - auc: 0.9684 - val_loss: 0.4549 - val_accuracy: 0.8758 - val_auc: 0.9685\n", + " - 2s - loss: 0.1412 - accuracy: 0.9348 - auc: 0.9770 - val_loss: 0.4540 - val_accuracy: 0.8894 - val_auc: 0.9770\n", "Epoch 79/100\n", - " - 2s - loss: 0.1749 - accuracy: 0.9191 - auc: 0.9685 - val_loss: 0.4467 - val_accuracy: 0.8744 - val_auc: 0.9686\n", + " - 2s - loss: 0.1376 - accuracy: 0.9347 - auc: 0.9770 - val_loss: 0.4912 - val_accuracy: 0.8868 - val_auc: 0.9771\n", "Epoch 80/100\n", - " - 2s - loss: 0.1716 - accuracy: 0.9209 - auc: 0.9686 - val_loss: 0.5058 - val_accuracy: 0.8704 - val_auc: 0.9686\n", + " - 2s - loss: 0.1438 - accuracy: 0.9308 - auc: 0.9771 - val_loss: 0.4600 - val_accuracy: 0.8878 - val_auc: 0.9771\n", "Epoch 81/100\n", - " - 2s - loss: 0.1755 - accuracy: 0.9200 - auc: 0.9687 - val_loss: 0.4596 - val_accuracy: 0.8717 - val_auc: 0.9687\n", + " - 2s - loss: 0.1385 - accuracy: 0.9349 - auc: 0.9771 - val_loss: 0.4933 - val_accuracy: 0.8889 - val_auc: 0.9772\n", "Epoch 82/100\n", - " - 2s - loss: 0.1783 - accuracy: 0.9195 - auc: 0.9687 - val_loss: 0.4513 - val_accuracy: 0.8709 - val_auc: 0.9688\n", + " - 2s - loss: 0.1377 - accuracy: 0.9355 - auc: 0.9772 - val_loss: 0.4626 - val_accuracy: 0.8920 - val_auc: 0.9772\n", "Epoch 83/100\n", - " - 2s - loss: 0.1743 - accuracy: 0.9172 - auc: 0.9688 - val_loss: 0.4850 - val_accuracy: 0.8771 - val_auc: 0.9688\n", + " - 2s - loss: 0.1387 - accuracy: 0.9339 - auc: 0.9773 - val_loss: 0.4790 - val_accuracy: 0.8910 - val_auc: 0.9773\n", "Epoch 84/100\n", - " - 2s - loss: 0.1705 - accuracy: 0.9220 - auc: 0.9689 - val_loss: 0.4736 - val_accuracy: 0.8717 - val_auc: 0.9689\n", + " - 2s - loss: 0.1367 - accuracy: 0.9361 - auc: 0.9773 - val_loss: 0.5121 - val_accuracy: 0.8847 - val_auc: 0.9773\n", "Epoch 85/100\n", - " - 2s - loss: 0.1737 - accuracy: 0.9173 - auc: 0.9690 - val_loss: 0.4596 - val_accuracy: 0.8731 - val_auc: 0.9690\n", + " - 2s - loss: 0.1346 - accuracy: 0.9344 - auc: 0.9774 - val_loss: 0.5133 - val_accuracy: 0.8931 - val_auc: 0.9774\n", "Epoch 86/100\n", - " - 2s - loss: 0.1696 - accuracy: 0.9215 - auc: 0.9690 - val_loss: 0.4649 - val_accuracy: 0.8682 - val_auc: 0.9691\n", + " - 2s - loss: 0.1391 - accuracy: 0.9332 - auc: 0.9774 - val_loss: 0.4980 - val_accuracy: 0.8915 - val_auc: 0.9775\n", "Epoch 87/100\n", - " - 2s - loss: 0.1723 - accuracy: 0.9215 - auc: 0.9691 - val_loss: 0.4383 - val_accuracy: 0.8673 - val_auc: 0.9691\n", + " - 2s - loss: 0.1400 - accuracy: 0.9338 - auc: 0.9775 - val_loss: 0.4740 - val_accuracy: 0.8941 - val_auc: 0.9775\n", "Epoch 88/100\n", - " - 2s - loss: 0.1737 - accuracy: 0.9195 - auc: 0.9692 - val_loss: 0.4937 - val_accuracy: 0.8596 - val_auc: 0.9692\n", + " - 2s - loss: 0.1399 - accuracy: 0.9354 - auc: 0.9775 - val_loss: 0.5174 - val_accuracy: 0.8915 - val_auc: 0.9776\n", "Epoch 89/100\n", - " - 2s - loss: 0.1733 - accuracy: 0.9210 - auc: 0.9692 - val_loss: 0.4626 - val_accuracy: 0.8758 - val_auc: 0.9693\n", + " - 2s - loss: 0.1393 - accuracy: 0.9334 - auc: 0.9776 - val_loss: 0.5047 - val_accuracy: 0.8857 - val_auc: 0.9776\n", "Epoch 90/100\n", - " - 2s - loss: 0.1696 - accuracy: 0.9194 - auc: 0.9693 - val_loss: 0.4623 - val_accuracy: 0.8677 - val_auc: 0.9693\n", + " - 2s - loss: 0.1332 - accuracy: 0.9373 - auc: 0.9776 - val_loss: 0.5035 - val_accuracy: 0.8889 - val_auc: 0.9776\n", "Epoch 91/100\n", - " - 2s - loss: 0.1683 - accuracy: 0.9199 - auc: 0.9694 - val_loss: 0.4461 - val_accuracy: 0.8735 - val_auc: 0.9694\n", + " - 2s - loss: 0.1377 - accuracy: 0.9334 - auc: 0.9777 - val_loss: 0.5216 - val_accuracy: 0.8873 - val_auc: 0.9777\n", "Epoch 92/100\n", - " - 2s - loss: 0.1710 - accuracy: 0.9171 - auc: 0.9694 - val_loss: 0.4808 - val_accuracy: 0.8731 - val_auc: 0.9695\n", + " - 2s - loss: 0.1401 - accuracy: 0.9346 - auc: 0.9777 - val_loss: 0.5004 - val_accuracy: 0.8926 - val_auc: 0.9777\n", "Epoch 93/100\n", - " - 2s - loss: 0.1677 - accuracy: 0.9181 - auc: 0.9695 - val_loss: 0.5076 - val_accuracy: 0.8664 - val_auc: 0.9695\n", + " - 2s - loss: 0.1376 - accuracy: 0.9364 - auc: 0.9778 - val_loss: 0.5153 - val_accuracy: 0.8915 - val_auc: 0.9778\n", "Epoch 94/100\n", - " - 2s - loss: 0.1714 - accuracy: 0.9207 - auc: 0.9696 - val_loss: 0.5061 - val_accuracy: 0.8744 - val_auc: 0.9696\n", + " - 2s - loss: 0.1355 - accuracy: 0.9381 - auc: 0.9778 - val_loss: 0.5621 - val_accuracy: 0.8894 - val_auc: 0.9778\n", "Epoch 95/100\n", - " - 2s - loss: 0.1651 - accuracy: 0.9228 - auc: 0.9696 - val_loss: 0.5127 - val_accuracy: 0.8700 - val_auc: 0.9697\n", + " - 2s - loss: 0.1353 - accuracy: 0.9390 - auc: 0.9778 - val_loss: 0.5273 - val_accuracy: 0.8878 - val_auc: 0.9779\n", "Epoch 96/100\n", - " - 2s - loss: 0.1709 - accuracy: 0.9202 - auc: 0.9697 - val_loss: 0.5105 - val_accuracy: 0.8700 - val_auc: 0.9697\n", + " - 2s - loss: 0.1340 - accuracy: 0.9386 - auc: 0.9779 - val_loss: 0.5277 - val_accuracy: 0.8863 - val_auc: 0.9779\n", "Epoch 97/100\n", - " - 2s - loss: 0.1702 - accuracy: 0.9208 - auc: 0.9697 - val_loss: 0.4978 - val_accuracy: 0.8717 - val_auc: 0.9698\n", + " - 2s - loss: 0.1359 - accuracy: 0.9374 - auc: 0.9779 - val_loss: 0.5054 - val_accuracy: 0.8931 - val_auc: 0.9780\n", "Epoch 98/100\n", - " - 2s - loss: 0.1704 - accuracy: 0.9205 - auc: 0.9698 - val_loss: 0.4820 - val_accuracy: 0.8717 - val_auc: 0.9698\n", + " - 2s - loss: 0.1370 - accuracy: 0.9350 - auc: 0.9780 - val_loss: 0.5338 - val_accuracy: 0.8884 - val_auc: 0.9780\n", "Epoch 99/100\n", - " - 2s - loss: 0.1717 - accuracy: 0.9192 - auc: 0.9699 - val_loss: 0.4921 - val_accuracy: 0.8758 - val_auc: 0.9699\n", + " - 2s - loss: 0.1350 - accuracy: 0.9354 - auc: 0.9780 - val_loss: 0.4918 - val_accuracy: 0.8894 - val_auc: 0.9780\n", "Epoch 100/100\n", - " - 2s - loss: 0.1699 - accuracy: 0.9221 - auc: 0.9699 - val_loss: 0.5117 - val_accuracy: 0.8709 - val_auc: 0.9699\n" + " - 2s - loss: 0.1401 - accuracy: 0.9333 - auc: 0.9781 - val_loss: 0.5212 - val_accuracy: 0.8936 - val_auc: 0.9781\n" ] } ], "source": [ - "history_wo=model_wo.fit(X_train_wo_sensor_resample.drop(['proportion','label'],axis=1).values, y_train_wo_sensor_resample.values,\n", - " validation_data=(X_test_wo_sensor.drop(['proportion','label'],axis=1).values, y_test_wo_sensor),\n", + "history_wo=model_wo.fit(X_train_wo_sensor_resample.drop(['malwareNum','proportion','label'],axis=1).values, y_train_wo_sensor_resample.values,\n", + " validation_data=(X_test_wo_sensor.drop(['malwareNum','proportion','label'],axis=1).values, y_test_wo_sensor),\n", " epochs=100,batch_size=32,verbose=2)\n", "# history = model.fit(X_train, y_train, validation_data=(X_test, y_test),epochs=50,batch_size=32, shuffle=True)" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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N7KbUv5tUfi8pSmhgJwOvPMGvOIPtUz9Eeu4bmLHg9bx27kksaveTe8xsbBw4ThAtLS3s3bv34ANDj8/o2wEDu0hHnhRQiAy7aSaXbmJPHey48pe8dW4HDZ6oNrMj5MBxIkqCRal/O/TvJBVFFGIvjfSn2qG+jcbGJtrr06RTKXbs2MXC06aPd6nNbIJw4BgnN9xwA6eeeuq+93F87nOfQxKPPvooO3bsIJ/P8xd/8RcsXbp0f6bCINHXQ6lvO+lSHkLspom+VCuZ5nbam+ppc4/CzGrMgQPgX5bD5qeP7jVPfi1cetMhDy9btozrr79+X+C45557ePjhh/nMZz5DW1sb27Zt44ILLuDd73oXGtxd7mVsLb/KpC8a2a0OUs1TaG+qpz2b9mM1zOyYceAYJ+eddx5bt25l48aNdHd309HRwcyZM/nMZz7Do48+SiqVYsOGDWx65l85ZVobEGyODvJ1U5jS2swp9RkHCzMbFw4ccNieQS29//3v595772Xz5s0sW7aMu+66i+6tW3n80YdJD2znNW94G7t7BylMPYlQiiknzfHktpmNOz8zehwtW7aMu+++m3vvvZf3v++97NraRWdrhmzfFh5+9DFe7trErqa5nDxjZvmtcA4aZnYccOAYR2effTZ79uxh1swZnJzeyX+87Lf59crVvPbSP+Cu7/2UM888kxltDdT5nRBmdhzxUNV4Kgzy1P/5Dsr3MlCEPR0L+edHfkFnaz3Z9IHBYsR7OMzMxoEDx3jp207sWk8pYGNMJ5edwqyOJg9Hmdlxz4HjWIsS7FoPfdvpp4H10cn09lZmN9d5lZSZnRBqOngu6RJJz0taK2n5CMc7JN0v6SlJv5Z0TsWxlyQ9LWmlpBUV6VMl/VDSmuS749WW75i//bBYgJ610LedrTGFVzSLuZ0dTGupr1nQmAxveDSzY6tmgUNSGrgFuBQ4C7hC0lnDTrsRWBkR5wIfA7487PhbImLRsFcXLgceiYgFwCPJ/pg1NDTQ09Nz7BrWwiBs+3ci18crpZPYW9fJ6Se10FhXu6GpiKCnp4eGBj/I0MyOnloOVS0B1kbEOgBJdwNLgdUV55wF/BVARDwnaZ6kGRGx5TDXXQpclGzfAfwEuGGshZs9ezZdXV10d3ePNevYFfOwdytBsK3URrpuGx1Nu1izrfZDUw0NDcyePbvmv2Nmk0ctA8csYH3FfhfwxmHnPAm8F/iZpCXAqcBsYAsQwA8kBfD3EXFbkmdGRGwCiIhNkk4a6cclXQNcAzB37tyDjmezWebPn/8qqzYGPS/AN5ayd7DI0r038LpFS/jC+19LJu0ltmZ2Yqpl6zXSP6eHjwvdBHRIWgn8EfAboJAcuzAizqc81PVpSW8ey49HxG0RsTgiFnd2do6t5EfLzvVw51IGBnNcvvdPOf/8N/L/fuB1DhpmdkKrZY+jC5hTsT8b2Fh5QkTsBq4CUHl2+MXkQ0RsTL63Srqf8tDXo8AWSTOT3sZMYGsN6/Dq9fbAne+m0LeTD/Z9lrlnnM9N7zuXtN/NbWYnuFr+0/cxYIGk+ZLqgGXAg5UnSJqSHAP4BPBoROyW1CypNTmnGXgbsCo570HgymT7SuCBGtbh1SmV4P5PEju7+ERxOXunns3fLFvkoGFmE0LNehwRUZB0LfB9IA3cHhHPSPpUcvxWYCFwp6Qi5Unzq5PsM4D7kyWqGeBbEfFwcuwm4B5JVwOvAB+oVR1etZ//Laz9Ibc0/SGP9y3ggY8tpq0hO96lMjM7KjQZ1vkvXrw4VqxYMfqJR8Mrv4RvXMZzU9/CJRuu4ht/sIS3nDni/L2Z2XFN0uPDbocA/JDDo6tvO9z7cXKts7li8xW87/w5DhpmNuE4cBxNP/trYs8m/kfdf6FU18aNl5053iUyMzvqHDiOlt2b4Ndf45VZ7+Turmksv/RMprXUj3epzMyOOgeOo+VfbyZKBa7d+HbOnzuFDy2eM3oeM7MTkJ+OezTseBke/yarTno3T7/Uwf9/+TmkvPTWzCYo9ziOhke/QCjFDd2X8Lu/1cnZp7SPd4nMzGrGgeNI9bwAK7/N2lM/xOreFq5+0zF4/pWZ2Thy4DhSj3+TkPiznrdy+kkt/M6C6eNdIjOzmnLgOBKlEqy6j50zf4efb0nz8Qvn+y1+ZjbhOXAcifW/gt1dfCd3AR1NWd57/qzxLpGZWc05cByJp/+JUqaBL3Ut4MNvnEtDtnZv8zMzO144cLxaxTys/mde6HgzvdHARy44dbxLZGZ2TDhwvFrrfgp9PTxY/A+ceXIrM9sbx7tEZmbHhAPHq7XqXqKhndu3nM6bTvdKKjObPBw4Xo18Pzz7XTbPehu9xTRv8hJcM5tEHDhejTU/gNxefpx5M3XpFG+cP228S2Rmdsz4WVWvxkv/Btlm7toyl9ef2kBjnVdTmdnkUdMeh6RLJD0vaa2k5SMc75B0v6SnJP1a0jlJ+hxJP5b0rKRnJF1XkedzkjZIWpl8LqtlHUa04XFyM17HM5t7PUxlZpNOzQKHpDRwC3ApcBZwhaSzhp12I7AyIs4FPgZ8OUkvAH8SEQuBC4BPD8v7pYhYlHweqlUdRlTIweaneLmh/JImT4yb2WRTyx7HEmBtRKyLiBxwN7B02DlnAY8ARMRzwDxJMyJiU0Q8kaTvAZ4Fjo/bsresgmKOXwzMo70xyzmz/CRcM5tcahk4ZgHrK/a7OLjxfxJ4L4CkJcCpwOzKEyTNA84DflWRfG0yvHW7pI6RflzSNZJWSFrR3d19RBU5wIbHAbhv6wwuPH0aab93w8wmmVoGjpFa1Bi2fxPQIWkl8EfAbygPU5UvILUA3wGuj4jdSfJXgdOARcAm4OaRfjwibouIxRGxuLOz8wiqMcyGJyg0drJydwsXepjKzCahWq6q6gIq3586G9hYeUISDK4CUPmxsi8mHyRlKQeNuyLivoo8W4a2JX0N+F6Nyj+yDY+zueUs2CHPb5jZpFTLHsdjwAJJ8yXVAcuABytPkDQlOQbwCeDRiNidBJGvA89GxF8PyzOzYvc9wKqa1WC4gV2w7d9Zkz2DxmyauVObjtlPm5kdL2rW44iIgqRrge8DaeD2iHhG0qeS47cCC4E7JRWB1cDVSfYLgY8CTyfDWAA3JiuoviBpEeVhr5eAT9aqDgfZuBIIVpZOY87URr97w8wmpZreAJg09A8NS7u1YvsXwIIR8v2MkedIiIiPHuViVi+ZGP+3vjnMnebehplNTn7kyFhseJyYehqrd6aZ42EqM5ukHDjGYsMT5GYsoi9X9PyGmU1aDhzV2r0R9myku+0cAAcOM5u0HDiqteEJANbVlx814sBhZpOVA0e1Nj8NSvFMaS4AszscOMxscnLgqNbeLdA4lRd3Fulsrfej1M1s0nLgqFbfNmiezivb+zxMZWaTmgNHtXp7oGk667f3O3CY2aTmwFGtvm2UmqaxaVe/7+Ews0nNgaNavdvYm55CKbyiyswmNweOapSK0L+DHbQCDhxmNrk5cFSjfwcQbCmWA8ecqY3jWx4zs3HkwFGN3m0AbMw1UZdOMaO1YZwLZGY2fhw4qtFXDhwv9Tcye2ojKb8u1swmMQeOaiQ9jjV76z2/YWaTngNHNZIex7O76xw4zGzSc+CoRm8PAK8MNDpwmNmkV9PAIekSSc9LWitp+QjHOyTdL+kpSb+WdM5oeSVNlfRDSWuS745a1gGAvm0U69ookPHNf2Y26dUscEhKA7cAlwJnAVdIOmvYaTcCKyPiXOBjwJeryLsceCQiFgCPJPu11buN/rpyfJrjp+Ka2SRXyx7HEmBtRKyLiBxwN7B02DlnUW78iYjngHmSZoySdylwR7J9B3B5DetQ1reNPakpgO/hMDOrZeCYBayv2O9K0io9CbwXQNIS4FRg9ih5Z0TEJoDk+6SRflzSNZJWSFrR3d19ZDXp7WFPup1sWrQ2ZI/sWmZmJ7haBo6RbnaIYfs3AR2SVgJ/BPwGKFSZ97Ai4raIWBwRizs7O8eS9WB95edU1aW9lsDMLFPDa3cBcyr2ZwMbK0+IiN3AVQCSBLyYfJoOk3eLpJkRsUnSTGBrbYq/r5DQ18OepnayGQcOM7NatoSPAQskzZdUBywDHqw8QdKU5BjAJ4BHk2ByuLwPAlcm21cCD9SwDjCwC0oF9qTa3OMwM6OGPY6IKEi6Fvg+kAZuj4hnJH0qOX4rsBC4U1IRWA1cfbi8yaVvAu6RdDXwCvCBWtUBgL7yPRy7Uu3UucdhZlbToSoi4iHgoWFpt1Zs/wJYUG3eJL0HuPjolvQwkseN7JQDh5kZ+M7x0SWPG9mBh6rMzMCBY3RJj2M7be5xmJnhwDG6pMfRE63ucZiZ4cAxut4eyDbTW8ySdeAwM3PgGFXfNmieRr5Y8lCVmRkOHKPr3QZN0xksOHCYmUGVgUPSBZJaK/ZbJb2xdsU6jvRtg6Zp5NzjMDMDqu9xfBXYW7Hfm6RNfL090DydXKHkyXEzM6oPHIqIfQ8ZjIgSNb558LjR1wNNyRyHA4eZWdWBY52kP5aUTT7XAetqWbDjQq4XCv37exweqjIzqzpwfAr4bWAD5afevhG4plaFOm4kN//RVA4cXo5rZlblcFNEbKX8hNrJJbn5j+bp5IvhHoeZGVUGDknfYIQXKUXEx496iY4nveUn40bTNHLFbgcOMzOqn+D+XsV2A/Aehr2UaUJKehy5+qlAN/UOHGZmVQ9VfadyX9K3gR/VpETHk2SOI1fXAUA2PdIbbc3MJpdX+0/oBcDco1mQ41LfNkhlyWdaALwc18yM6u8c3yNpd/LZBXwX+NMq8l0i6XlJayUtH+F4u6TvSnpS0jOSht4/foaklRWf3ZKuT459TtKGimOXjanGYyKY+hpyxfL0Tl0mXbufMjM7QVQ7VNUqaSrlnkbDUPLh8khKA7cAb6W8hPcxSQ9GxOqK0z4NrI6Id0nqBJ6XdFdEPA8sqrjOBuD+inxfiogvVlP2I/LWP4e3/jm5nj4AT46bmVH9qqpPANcBs4GVwAXAL4DfO0y2JcDaiFiXXONuYCnld4sPCaBVkoAWYDtQGHadi4EXIuLlaspaC7liEfAch5kZVD/HcR3wBuDliHgLcB7QPUqeWcD6iv2uJK3SV4CFlFdoPQ1clzzOpNIy4NvD0q6V9JSk2yV1VFmHVy1XKHeuvKrKzKz6wDEQEQMAkuoj4jngjFHyjPTP8+HDW2+n3IM5hfLQ1Fckte27gFQHvBv4p4o8XwVOS87fBNw84o9L10haIWlFd/doMe7wcsVyLPNQlZlZ9YGjS9IU4J+BH0p6gNHv4+gC5lTszx4hz1XAfVG2FngROLPi+KXAExGxZSghIrZERDHpmXyN8pDYQSLitohYHBGLOzs7R63g4eQK5cDhR46YmVU/Of6eZPNzkn4MtAMPj5LtMWCBpPmUJ7eXAR8eds4rlOcw/lXSDMq9mMqHJ17BsGEqSTMjYlOy+x5gVTV1OBL5oR6HA4eZ2dgfjR4RP63yvIKka4HvA2ng9oh4RtKnkuO3Ap8HvinpacpDWzdExDYASU2UV2R9ctilvyBpEeVhr5dGOH7UDfU4PFRlZlbjd2pExEPAQ8PSbq3Y3gi87RB5+4BpI6R/9CgXc1SDDhxmZvu4JaxCzkNVZmb7uCWsQt49DjOzfdwSVsHLcc3M9nNLWIV9k+MeqjIzc+CoxtBy3Kx7HGZmDhzVGHSPw8xsH7eEVfBQlZnZfm4Jq5ArlsikRCrlp+OamTlwVCFfKHlFlZlZwq1hFXJFBw4zsyFuDauQK5Q8v2FmlnBrWIVcoeRHqpuZJdwaViFXLPntf2ZmCbeGVch5ctzMbB+3hlXw5LiZ2X5uDauQL3qOw8xsiFvDKnhVlZnZfjVtDSVdIul5SWslLR/heLuk70p6UtIzkq6qOPaSpKclrZS0oiJ9qqQfSlqTfHfUsg7gOQ4zs0o1aw0lpYFbgEuBs4ArJJ017LRPA6sj4nXARcDNkuoqjr8lIhZFxOKKtOXAIxGxAHgk2a+pQS/HNTPbp5at4RJgbUSsi4gccDewdNg5AbRKEtACbAcKo1x3KXBHsn0HcPlRK/Eh5L0c18xsn1q2hrOA9RX7XUlapa8AC4GNwNPAdRFRSo4F8ANJj0u6piLPjIjYBJB8nzTSj0u6RtIKSSu6u7uPqCJeVWVmtl8tW8ORHiUbw/bfDqwETgEWAV+R1JYcuzAizqc81PVpSW8ey49HxG0RsTgiFnd2do6p4MN5ctzMbL9atoZdwJyK/dmUexaVrgLui7K1wIvAmQARsTH53grcT3noC2CLpJkAyffWmtUgkSuUyGb8SHUzM6ht4HgMWCBpfjLhvQx4cNg5rwAXA0iaAZwBrJPULKk1SW8G3gasSvI8CFyZbF8JPFDDOgCQLwZ16XStf8bM7ISQqdWFI6Ig6Vrg+0AauD0inpH0qeT4rcDngW9Kepry0NYNEbFN0muA+8tz5mSAb0XEw8mlbwLukXQ15cDzgVrVYYiX45qZ7VezwAEQEQ8BDw1Lu7VieyPl3sTwfOuA1x3imj0kvZRjISI8OW5mVsGt4SjyxfJ8fl3acxxmZuDAMapcsbw62D0OM7Myt4ajyBWSwOHluGZmgAPHqIYCR9Y9DjMzwIFjVPmiexxmZpXcGo5isOA5DjOzSm4NRzE0VOWHHJqZlbk1HMXQUJUfq25mVubWcBRejmtmdiC3hqPwclwzswO5NRxFzpPjZmYHcGs4ipznOMzMDuDWcBReVWVmdiC3hqPwUJWZ2YHcGo7CQ1VmZgdyaziKvJfjmpkdwK3hKDxUZWZ2oJq2hpIukfS8pLWSlo9wvF3SdyU9KekZSVcl6XMk/VjSs0n6dRV5Pidpg6SVyeeyWtZh0PdxmJkdoGavjpWUBm4B3gp0AY9JejAiVlec9mlgdUS8S1In8Lyku4AC8CcR8YSkVuBxST+syPuliPhircpeyU/HNTM7UC1bwyXA2ohYFxE54G5g6bBzAmiVJKAF2A4UImJTRDwBEBF7gGeBWTUs6yHlCiUyKZFK+dWxZmZQ28AxC1hfsd/FwY3/V4CFwEbgaeC6iChVniBpHnAe8KuK5GslPSXpdkkdI/24pGskrZC0oru7+1VXIlcoeX7DzKxCLVvEkf6JHsP23w6sBE4BFgFfkdS27wJSC/Ad4PqI2J0kfxU4LTl/E3DzSD8eEbdFxOKIWNzZ2fmqK5ErOnCYmVWqZYvYBcyp2J9NuWdR6SrgvihbC7wInAkgKUs5aNwVEfcNZYiILRFRTHomX6M8JFYz+WLJ93CYmVWoZYv4GLBA0nxJdcAy4MFh57wCXAwgaQZwBrAumfP4OvBsRPx1ZQZJMyt23wOsqlH5gfKqKk+Mm5ntV7NVVRFRkHQt8H0gDdweEc9I+lRy/Fbg88A3JT1NeWjrhojYJulNwEeBpyWtTC55Y0Q8BHxB0iLKw14vAZ+sVR2gPMfh51SZme1Xs8ABkDT0Dw1Lu7VieyPwthHy/YyR50iIiI8e5WIeVq7goSozs0puEUeR9+S4mdkB3CKOwquqzMwO5BZxFDlPjpuZHcAt4ihyxSDrHoeZ2T5uEUfhHoeZ2YHcIo4iVyh6Oa6ZWQW3iKPw5LiZ2YHcIo4iXwiyaT8Z18xsiAPHKNzjMDM7kFvEUZQnx9PjXQwzs+OGA8cocoUS2YyHqszMhjhwHEZEkCuWqPdyXDOzfdwiHka+WH7vlOc4zMz2c4t4GLli+S22DhxmZvu5RTyMfKEcOPxYdTOz/dwiHoZ7HGZmB3OLeBi5pMfhZ1WZme1X0xZR0iWSnpe0VtLyEY63S/qupCclPSPpqtHySpoq6YeS1iTfHbUq/2DBPQ4zs+Fq1iJKSgO3AJcCZwFXSDpr2GmfBlZHxOuAi4CbJdWNknc58EhELAAeSfZrIl90j8PMbLhatohLgLURsS4icsDdwNJh5wTQKklAC7AdKIySdylwR7J9B3B5rSqQc4/DzOwgtWwRZwHrK/a7krRKXwEWAhuBp4HrIqI0St4ZEbEJIPk+aaQfl3SNpBWSVnR3d7+qCnhy3MzsYLVsEUd6TkcM2387sBI4BVgEfEVSW5V5DysibouIxRGxuLOzcyxZ9/FyXDOzg9WyRewC5lTsz6bcs6h0FXBflK0FXgTOHCXvFkkzAZLvrTUoOwCD7nGYmR2kli3iY8ACSfMl1QHLgAeHnfMKcDGApBnAGcC6UfI+CFyZbF8JPFCrCng5rpnZwTK1unBEFCRdC3wfSAO3R8Qzkj6VHL8V+DzwTUlPUx6euiEitgGMlDe59E3APZKuphx4PlCrOgwFDr861sxsv5oFDoCIeAh4aFjarRXbG4G3VZs3Se8h6aXU2tByXM9xmJnt5xbxMLwc18zsYG4RD8PLcc3MDuYW8TDc4zAzO5hbxMPI+ZEjZmYHcYt4GF6Oa2Z2MLeIh5ErlMikRCo10o3sZmaTkwPHYeSLJS/FNTMbxq3iYeQKJU+Mm5kNU9MbAE90C2e20Z8vjncxzMyOKw4ch7FsyVyWLZk73sUwMzuueBzGzMzGxIHDzMzGxIHDzMzGxIHDzMzGxIHDzMzGxIHDzMzGxIHDzMzGxIHDzMzGRBEx3mWoOUndwMuvMvt0YNtRLM6JYjLWezLWGSZnvSdjnWHs9T41IjqHJ06KwHEkJK2IiMXjXY5jbTLWezLWGSZnvSdjneHo1dtDVWZmNiYOHGZmNiYOHKO7bbwLME4mY70nY51hctZ7MtYZjlK9PcdhZmZj4h6HmZmNiQOHmZmNiQPHYUi6RNLzktZKWj7e5akFSXMk/VjSs5KekXRdkj5V0g8lrUm+O8a7rEebpLSk30j6XrI/Geo8RdK9kp5L/p//h4leb0mfSf5sr5L0bUkNE7HOkm6XtFXSqoq0Q9ZT0meTtu15SW8fy285cByCpDRwC3ApcBZwhaSzxrdUNVEA/iQiFgIXAJ9O6rkceCQiFgCPJPsTzXXAsxX7k6HOXwYejogzgddRrv+ErbekWcAfA4sj4hwgDSxjYtb5m8Alw9JGrGfyd3wZcHaS5++SNq8qDhyHtgRYGxHrIiIH3A0sHecyHXURsSkinki291BuSGZRrusdyWl3AJePSwFrRNJs4B3AP1QkT/Q6twFvBr4OEBG5iNjJBK835VdkN0rKAE3ARiZgnSPiUWD7sORD1XMpcHdEDEbEi8Baym1eVRw4Dm0WsL5ivytJm7AkzQPOA34FzIiITVAOLsBJ41i0Wvgb4E+BUkXaRK/za4Bu4BvJEN0/SGpmAtc7IjYAXwReATYBuyLiB0zgOg9zqHoeUfvmwHFoGiFtwq5dltQCfAe4PiJ2j3d5aknSO4GtEfH4eJflGMsA5wNfjYjzgF4mxhDNISVj+kuB+cApQLOkj4xvqY4LR9S+OXAcWhcwp2J/NuUu7oQjKUs5aNwVEfclyVskzUyOzwS2jlf5auBC4N2SXqI8BPl7kv6RiV1nKP+Z7oqIXyX791IOJBO53r8PvBgR3RGRB+4DfpuJXedKh6rnEbVvDhyH9hiwQNJ8SXWUJ5IeHOcyHXWSRHnM+9mI+OuKQw8CVybbVwIPHOuy1UpEfDYiZkfEPMr/X/9PRHyECVxngIjYDKyXdEaSdDGwmold71eACyQ1JX/WL6Y8jzeR61zpUPV8EFgmqV7SfGAB8OtqL+o7xw9D0mWUx8LTwO0R8ZfjW6KjT9KbgH8Fnmb/eP+NlOc57gHmUv7L94GIGD7xdsKTdBHwXyLinZKmMcHrLGkR5QUBdcA64CrK/4CcsPWW9OfAhyivIPwN8AmghQlWZ0nfBi6i/Oj0LcCfAf/MIeop6b8CH6f83+X6iPiXqn/LgcPMzMbCQ1VmZjYmDhxmZjYmDhxmZjYmDhxmZjYmDhxmZjYmDhxmxzlJFw09wdfseODAYWZmY+LAYXaUSPqIpF9LWinp75P3feyVdLOkJyQ9IqkzOXeRpF9KekrS/UPvSZB0uqQfSXoyyXNacvmWivdo3JXcBW02Lhw4zI4CSQsp3518YUQsAorAfwSagSci4nzgp5Tv5gW4E7ghIs6lfNf+UPpdwC0R8TrKz1TalKSfB1xP+d0wr6H8vC2zcZEZ7wKYTRAXA68HHks6A42UHyhXAv53cs4/AvdJagemRMRPk/Q7gH+S1ArMioj7ASJiACC53q8joivZXwnMA35W81qZjcCBw+zoEHBHRHz2gETpvw8773DP+Dnc8NNgxXYR/921ceShKrOj4xHg/ZJOgn3vej6V8t+x9yfnfBj4WUTsAnZI+p0k/aPAT5P3oHRJujy5Rr2kpmNZCbNq+F8tZkdBRKyW9N+AH0hKAXng05RflnS2pMeBXZTnQaD8iOtbk8Aw9JRaKAeRv5f0P5NrfOAYVsOsKn46rlkNSdobES3jXQ6zo8lDVWZmNibucZiZ2Zi4x2FmZmPiwGFmZmPiwGFmZmPiwGFmZmPiwGFmZmPyfwGX3dQjA5FfrgAAAABJRU5ErkJggg==\n", 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" ] @@ -1703,11 +1716,11 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ - "y_pred_wo_sensor = model_wo.predict(X_test_wo_sensor.drop(['proportion','label'],axis=1).values)\n", + "y_pred_wo_sensor = model_wo.predict(X_test_wo_sensor.drop(['malwareNum','proportion','label'],axis=1).values)\n", "y_pred_repack_benign_wo_sensor = model_wo.predict_proba(repackaged_benign_test_X_wo_sensors)\n", "# covid_y_pred_wo_sensor = model_wo.predict_proba(covid_test_X_wo_sensors)" ] @@ -1721,81 +1734,30 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC (including sensor features) 0.9302093317826264\n", - "AUC (not including sensor features) 0.9217231661142387\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize = (10, 6))\n", - "fpr, tpr, _ = roc_curve(y_test, y_pred_with_sensor)\n", - "fpr2, tpr2, _2 = roc_curve(y_test_wo_sensor, y_pred_wo_sensor)\n", - "plt.plot(fpr, tpr, lw=2, label='With sensor features')\n", - "plt.plot(fpr2, tpr2, lw=2, label='Without sensor features')\n", - "print('AUC (including sensor features)', roc_auc_score(y_test, y_pred_with_sensor))\n", - "print('AUC (not including sensor features)', roc_auc_score(y_test, y_pred_wo_sensor))\n", - "\n", - "plt.xlabel('False Positive Rate', fontsize = 13)\n", - "plt.ylabel('True Positive Rate', fontsize = 13)\n", - "plt.title('ROC Curve', fontsize = 15)\n", - "plt.legend(fontsize = 13)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "AUC (including sensor features) 0.9298030211513315\n", - "AUC (not including sensor features) 0.9166320120966037\n" + "AUC (including sensor features) 0.9573006766428322\n", + "AUC (not including sensor features) 0.9320767424504433\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 43, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1823,19 +1785,19 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([0.59328358, 0.95100354]),\n", - " array([0.79301746, 0.88080919]),\n", - " array([0.67876201, 0.91456145]),\n", - " array([ 401, 1829]))" + "(array([0.74770642, 0.94904891]),\n", + " array([0.81296758, 0.9270073 ]),\n", + " array([0.77897252, 0.93789862]),\n", + " array([ 401, 1507]))" ] }, - "execution_count": 47, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1846,19 +1808,19 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([0.61746362, 0.94053745]),\n", - " array([0.74064838, 0.89939858]),\n", - " array([0.67346939, 0.91950811]),\n", - " array([ 401, 1829]))" + "(array([0.73684211, 0.93758389]),\n", + " array([0.7680798, 0.9270073]),\n", + " array([0.75213675, 0.9322656 ]),\n", + " array([ 401, 1507]))" ] }, - "execution_count": 48, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1869,7 +1831,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1882,7 +1844,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1901,15 +1863,15 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Balanced Average Accuracy (including sensor features): 0.8369133208531432\n", - "Balanced Average Accuracy (not including sensor features): 0.8200234787552715\n" + "Balanced Average Accuracy (including sensor features): 0.8699874401587273\n", + "Balanced Average Accuracy (not including sensor features): 0.847543549884413\n" ] } ], @@ -1927,14 +1889,18 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "pairs = pd.read_csv('../../repackaging_pairs.txt')\n", "# Train test split\n", - "X_ = pd.read_csv('../data/Training_Data/Training_Dataset.csv')\n", - "y_ = pd.read_csv('../data/Training_Data/Labels.csv')\n", + "X_ = pd.read_csv('../data/Training_Data/Training_Dataset_with_threshold.csv')\n", + "y_ = pd.read_csv('../data/Training_Data/Labels_trainingset.csv')\n", + "\n", + "# X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", + "# covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", "\n", "X_train_, X_test_, y_train_, y_test_ = train_test_split(X_,y_['label'], \n", " test_size = 0.2, \n", @@ -1944,7 +1910,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1981,7 +1947,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1997,16 +1963,16 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1743" + "1585" ] }, - "execution_count": 53, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -2041,6 +2007,30 @@ "print(\"Accuracy for non-paired apps (not including sensor features):\", np.sum(non_paired_wo_sensor_accuracy)/len(non_paired_wo_sensor_accuracy))" ] }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for paired apps (including sensor features): 0.8571428571428571\n", + "Accuracy for non-paired apps (including sensor features): 0.9072696050372067\n", + "Accuracy for paired apps (not including sensor features): 0.8509316770186336\n", + "Accuracy for non-paired apps (not including sensor features): 0.8975386376645679\n" + ] + } + ], + "source": [ + "print(\"Accuracy for paired apps (including sensor features):\", np.sum(paired_w_sensor_accuracy)/len(paired_w_sensor_accuracy))\n", + "print(\"Accuracy for non-paired apps (including sensor features):\", np.sum(non_paired_w_sensor_accuracy)/len(non_paired_w_sensor_accuracy))\n", + "\n", + "print(\"Accuracy for paired apps (not including sensor features):\", np.sum(paired_wo_sensor_accuracy)/len(paired_wo_sensor_accuracy))\n", + "print(\"Accuracy for non-paired apps (not including sensor features):\", np.sum(non_paired_wo_sensor_accuracy)/len(non_paired_wo_sensor_accuracy))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2060,15 +2050,15 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy (including sensor features): 0.8075577326801959\n", - "Accuracy (without sensor features): 0.8684394681595521\n" + "Accuracy (including sensor features): 0.8848845346396081\n", + "Accuracy (without sensor features): 0.9688593421973408\n" ] } ], @@ -2669,12 +2659,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ - "def set_label(X,thres):\n", - " X_tp=X\n", + "def set_label_ratio(X,thres):\n", + " X_tp=X.copy()\n", " X_tp['label']=0\n", " X_tp.loc[X_tp['proportion']>=thres,'label']=1\n", " print(sum(X_tp['label'])/X_tp.shape[0])\n", @@ -2683,28 +2673,42 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "def set_label_count(X,thres):\n", + " X_tp=X.copy()\n", + " X_tp['label']=0\n", + " X_tp.loc[X_tp['malwareNum']>=thres,'label']=1\n", + " print(sum(X_tp['label'])/X_tp.shape[0])\n", + " return X_tp" + ] + }, + { + "cell_type": "code", + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ - "def build_model(X_train,X_test):\n", + "def build_model(X_train,X_test,drop_lst=[]):\n", " tf.keras.backend.clear_session()\n", " model = keras.Sequential()\n", " model = Sequential()\n", - " model.add(Dense(64, input_dim=X_train.shape[1]-2, activation='relu'))\n", + " model.add(Dense(32, input_dim=X_train.shape[1]-2, activation='relu'))\n", " model.add(Dropout(0.4))\n", - " model.add(Dense(16, activation='relu'))\n", + " model.add(Dense(48, activation='relu'))\n", " model.add(Dropout(0.4))\n", " # model.add(Dense(128, activation='relu'))\n", " model.add(Dense(1, activation='sigmoid'))\n", " # Compile model\n", " model.compile(loss='binary_crossentropy', optimizer=adam(lr=0.0001), metrics=['accuracy',tf.keras.metrics.AUC()])\n", - " history=model.fit(X_train.drop(['proportion','label'],axis=1).values, X_train['label'].values,\n", + " history=model.fit(X_train.drop(drop_lst,axis=1).values, X_train['label'].values,\n", " # validation_split=0.2,\n", - " validation_data=(X_test.drop(['proportion','label'],axis=1).values, X_test['label']),\n", + " validation_data=(X_test.drop(drop_lst,axis=1).values, X_test['label']),\n", " verbose=0,\n", " epochs=100,batch_size=32)\n", - " y_pred=model.predict(X_test.drop(['proportion','label'],axis=1))\n", + " y_pred=model.predict(X_test.drop(drop_lst,axis=1))\n", " return model,y_pred\n", " # history = model.fit(X_train, y_train, validation_split=0.2,epochs=50,batch_size=32, shuffle=True)\n", " " @@ -2712,1223 +2716,283 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ - "def compute_metric_thres(X_train,X_test,thres):\n", - " X_train_tp=set_label(X_train,thres)\n", - " X_test_tp=set_label(X_test,thres)\n", + "def compute_metric_thres(X_train,X_test,thres,drop_lst=[],mode=1):\n", + " if mode==1:\n", + " X_train_tp=set_label_ratio(X_train,thres)\n", + " X_test_tp=set_label_ratio(X_test,thres)\n", + " else:\n", + " X_train_tp=set_label_count(X_train,thres)\n", + " X_test_tp=set_label_count(X_test,thres)\n", + " \n", " X_train_resample_tp,y_train_resample_tp=resample(X_train_tp)\n", - " print(X_train_resample_tp.columns)\n", - " model_with_sensor,y_pred_with_sensor = build_model(X_train_resample_tp,X_test_tp)\n", + "# print(X_train_resample_tp.columns)\n", + " model_with_sensor,y_pred_with_sensor = build_model(X_train_resample_tp,X_test_tp,drop_lst)\n", " score=balanced_accuracy_score(X_test_tp['label'], np.round(y_pred_with_sensor))\n", "# print('Balanced Average Accuracy (including sensor features):', score)\n", " metrics=precision_recall_fscore_support(X_test_tp['label'], np.round(y_pred_with_sensor))\n", " recall=metrics[1]\n", " prcision=metrics[0]\n", " print('balanced acc=%s recall_benign=%s recall_malware=%s'%(str(score),str(recall[0]),str(recall[1])))\n", - " return score,recall[0],recall[1],prcision[0],prcision[1]\n" + " return score,recall[0],recall[1],prcision[0],prcision[1]" ] }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, + "execution_count": 66, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "------thres= 0.01\n" + "------thres= 0.01\n", + "0.7897771952817825\n", + "0.789832285115304\n", + "balanced acc=0.879812744184661 recall_benign=0.8478802992518704 recall_malware=0.9117451891174518\n", + "------thres= 0.03\n", + "0.7896461336828309\n", + "0.789308176100629\n", + "balanced acc=0.8896701750213079 recall_benign=0.8756218905472637 recall_malware=0.9037184594953519\n", + "------thres= 0.049999999999999996\n", + "0.7870249017038008\n", + "0.7861635220125787\n", + "balanced acc=0.8699803921568627 recall_benign=0.8186274509803921 recall_malware=0.9213333333333333\n", + "------thres= 0.06999999999999999\n", + "0.7815203145478374\n", + "0.7809224318658281\n", + "balanced acc=0.8756045085257378 recall_benign=0.8444976076555024 recall_malware=0.9067114093959732\n", + "------thres= 0.08999999999999998\n", + "0.7745740498034076\n", + "0.7772536687631028\n", + "balanced acc=0.8733148228947681 recall_benign=0.8329411764705882 recall_malware=0.9136884693189481\n", + "------thres= 0.10999999999999997\n", + "0.7623853211009174\n", + "0.760482180293501\n", + "balanced acc=0.8583117053507201 recall_benign=0.8227571115973742 recall_malware=0.8938662991040661\n", + "------thres= 0.12999999999999998\n", + "0.7428571428571429\n", + "0.7389937106918238\n", + "balanced acc=0.8394984192087499 recall_benign=0.7811244979919679 recall_malware=0.8978723404255319\n", + "------thres= 0.15\n", + "0.7193971166448231\n", + "0.720125786163522\n", + "balanced acc=0.8311867261992378 recall_benign=0.7846441947565543 recall_malware=0.8777292576419214\n", + "------thres= 0.16999999999999998\n", + "0.6657929226736566\n", + "0.6645702306079665\n", + "balanced acc=0.8242754337539432 recall_benign=0.803125 recall_malware=0.8454258675078864\n", + "------thres= 0.18999999999999997\n", + "0.608781127129751\n", + "0.6158280922431866\n", + "balanced acc=0.8174990566311573 recall_benign=0.7830832196452933 recall_malware=0.8519148936170213\n", + "------thres= 0.20999999999999996\n", + "0.5636959370904325\n", + "0.5718029350104822\n", + "balanced acc=0.8229185715551857 recall_benign=0.7649938800489596 recall_malware=0.8808432630614116\n", + "------thres= 0.22999999999999998\n", + "0.5174311926605505\n", + "0.5178197064989518\n", + "balanced acc=0.8070014082027812 recall_benign=0.7152173913043478 recall_malware=0.8987854251012146\n", + "------thres= 0.24999999999999997\n", + "0.4748361730013106\n", + "0.470125786163522\n", + "balanced acc=0.7861880518311947 recall_benign=0.6805143422354105 recall_malware=0.8918617614269788\n", + "------thres= 0.26999999999999996\n", + "0.3871559633027523\n", + "0.3731656184486373\n", + "balanced acc=0.7548053436548796 recall_benign=0.5994983277591973 recall_malware=0.9101123595505618\n", + "------thres= 0.29\n", + "0.2916120576671035\n", + "0.28354297693920333\n", + "balanced acc=0.741527583777637 recall_benign=0.8046817849305048 recall_malware=0.678373382624769\n", + "------thres= 0.30999999999999994\n", + "0.21874180865006554\n", + "0.21016771488469602\n", + "balanced acc=0.7707779324085274 recall_benign=0.8108825481088254 recall_malware=0.7306733167082294\n", + "------thres= 0.32999999999999996\n", + "0.17116644823066843\n", + "0.15828092243186584\n", + "balanced acc=0.7967637914113466 recall_benign=0.8518057285180572 recall_malware=0.7417218543046358\n", + "------thres= 0.35\n", + "0.1436435124508519\n", + "0.1278825995807128\n", + "balanced acc=0.8038402427490543 recall_benign=0.8617788461538461 recall_malware=0.7459016393442623\n", + "------thres= 0.36999999999999994\n", + "0.11651376146788991\n", + "0.1090146750524109\n", + "balanced acc=0.8074943438914027 recall_benign=0.8794117647058823 recall_malware=0.7355769230769231\n", + "------thres= 0.38999999999999996\n", + "0.09541284403669725\n", + "0.09014675052410902\n", + "balanced acc=0.7929080484406816 recall_benign=0.8997695852534562 recall_malware=0.686046511627907\n" ] - }, + } + ], + "source": [ + "import collections\n", + "pd_metric=collections.defaultdict(list)\n", + "for thres in list(np.arange(0.01,0.4,0.02)):\n", + " print('------thres=',thres)\n", + " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train.drop('malwareNum',axis=1),\n", + " X_test.drop('malwareNum',axis=1),thres,['proportion','label'],1)\n", + " pd_metric['thres'].append(thres)\n", + " pd_metric['balanced_accuracy'].append(score)\n", + " pd_metric['benign_recall'].append(recall1)\n", + " pd_metric['malware_recall'].append(recall2)\n", + " pd_metric['benign_precision'].append(precision1)\n", + " pd_metric['malware_precision'].append(precision2)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(pd_metric).to_csv('../../dnn_threshold.csv',index=False)\n", + "df=pd.DataFrame(pd_metric)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8087707492148946\n" - ] + "data": { + "image/png": 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Z2Rn8ePJH5h2fh6+DL7O6zsLX0dfQsgqlYtxfA1jaQZtX4blV8GYIBL5XctPPxdoJhiyBnv+DCxvh+04QcVg/bVdy+jRzZ+24DlS1tWD4zwdZtO+yoSUVma1nbvD26pN0rOvC3OdalmvTB3B30tI2ZGcb3xofY+Ri/EWG/z2c7459x+O+j7O0z1KjNn2oSMYP0GmiFpJZzKXP/17/l9iU2IJfFALavgbPb9QmjH/qCQe/1/4uKVJCbCicWK7NSVzYApHH4HYkZKaXvH0jp041O9aMbU9gAzemrz/F9nPRhpakM4ev3GLs0iM0cndg7nMtK8RchbuDFelZ2dxMrvjfvZKQlZ3FgpAFDF4/mMikSL7s8iWfdPykTJKslZSKM9RTQlaeX8kH+z+gnUc7vu/+feEFPQO0oZ81r8KGt+DKXm0OwaoIMc934rQ7hvBDWsRRxGFIjS+8vJUT2LqCXTVtYZlttZznrtq/tjnH7aqBZfkMkbS1NGPO0OYMmruf8b8dZe3Y9tRytTO0rIcSGp3IC4sOUd3BigWjW5WrSdyHkRvLfz0hBVd7lWqjIK7cvsK0PdM4FnOMQO9A3m3zrlFF7TyKivFNLSEbLm1gxv4ZOFs5sy9yH+fizlHfuX7hFWyc4ZnfYP/XsOUDLfZ/8CJwLyBGNzMdbpzUDD48GCKCIS5Me02YgGtDLdrIMwBqtARza0iKgeQYSI6G5FhIis55HgPRZyBpZ+E/FN5todf/tLmJcoaNhRnzh7ek/7d7GbP4MKtfa1dg3nhjICohlRE//YuZiQm/PP9YhcpF5JG7IUt8Ks08DSzGyMiW2fx29je+OvwV5qbm/K/j/+jj28fgC7KKSqU3/l3hu5i6eyr+bv581ukz+q7uy8JTC/lfx/89vKKJCbR/Q0vxsGI0/NhdM9w6gfea/PUTkJWT6dKuumbw/iOgRoBmzpYF9Gqdaz1aeGa6NpGdHHP3hyLhGvw7H+Z3hebDtHkO+/IVWeLlbMM3z7Zg+E//8ubvx5k/vGWJUhCUBgl3Mhj587/cTs1k2Zg2FW7vAXcnbRFXlFrEdQ/hieG8t+89DkUdomONjkxvN51qNuVzi8pKbfyHog4xYccE6jnX45tu32BnYceguoNYdnYZb/i/QXXb6o9uxLsNvLIHVo+BvybcPW5mDR7Nc0JCW2mG71BDf6GgZhbg4KE98vPYK7BrJhyYC6fXaPMebV4Ds/LTI21X24VpfRrywfrTfLX1AhO61zO0pDxSM7J46ZdgwmKTWDCqdYVMa1DV1gILUxOuq5BOQNs3YcX5FXwR/AVCCGa0m8GTdZ4sd738/FRa4w+JDWHc1nF42nkyL2gedhZaz3t4o+H8dvY3Fp9ezKRWk3RrzLYqPLtCy/2TmaIZfbVGYGqAYQorB+jxIbQcBZve1VYfH14IPf4PGvQtN2sQRrWryanI28zZeoFG7vb0auJuaElkZUveWHaUfy/HMWdo+Q09fRRCCKo7qlh+0BZjvb/vffZF7qONextmtJuBu53hv4slpfyHIBSD0FuhvLLlFapYVWF+j/n37HrjYedBz5o9WXl+JbfTb+veqImJtiq41YvaWL8hTD8/VWvD0KUwfI129/H7c7CoH0SFGFaXjggh+L8nm+Dn5cSE5cc5F5VoUD1SSt5dG8LGUzd4r2+jCplqIj/ujpV79a6UkjWhaxiwdgBHo48y7bFpzO8+v0KYPlRC47+WeI0xm8dgYWLBDz1+KHCMbnST0dzJvMOKcysMoFDP1O6qDUX1/hxuhMD3HeHPN7VJYyPHytyU759ria2lGWMWBxN/x3DhhXO2hrL04FVe6Vyb5zsYd4y2PvBwsq60Qz0xd2IYv2087+59l3pV6rGq3yqGNDB8Rk19UqmM/0byDV7a9BIZ2RnM7z4fL3uvAss1cG5AG/c2LDmzhPSsChDLbGqmzTW8fgRaj4HDi2COP+z/1ujXCVR3tGLec/5Exqfw+m9HyczKfnQlPbP04FVmbTnPQP8aTO71kGivCoS7oxVRt1PJqkSLuKSU/B32NwPWDWD/9f1MCpjEgl4L8HIo2CfKM5XG+G+l3mLM5jHEp8UzL2gedao8fFXv6MajiU2J5a+wv8pIYRlg4wyPfwqv7dcmmzdOhblt4fwmQyt7KC19nPmwfxN2X4hl5sZzZXrujaeimLbmJF3qu/LpoGYVqtf3MNydrMnKlsQmpT26cDknJTOF9RfXM3rjaCbvnoyPvQ/L+y1nROMRmIiKaZGVYnI3MT2RV7a8QkRSBHOD5tLYpfEj67T1aEv9KvVZeGoh/ev0r1hfANf6WmqLC5s081/6tLbiuefH2mtGyDOtvTkVeZvvd4XRyMOB/s1rlPo5D12OY/xvR2nq6cR3w/z1stFJecHd4e5OXG4OD24CX96RUnIi9gSrL6zmn8v/kJyRjJe9F2+1eouhDYYaXTZNfVOx3x3ar/m4reM4H3ee2d1m06p6K53qCSEY2XgkU/dMZXf4bjp7dS5lpWWMEFCvJ9TqCod+gB2fwndtof146DJVCxc1Mt7t24hzUYm8tfIEtV3tSjWU8vyNRF5YeIgaTtYsGNUKG4sK/1/lHnJj+a8npFL+lgIWTmxKLH9e/JPVoasJSwjD2sya7j7dGVBnAC3dWlaaO7oK3YXJyMpgwo4JHI0+yv86/o9Onp2KVL+Xby+q21ZnwakFpaTQCDCz0FJNjz+ibUyzZxb83ANuXjS0sgewMDPhu+f8qWprwZhfgkttGCIyPoWRP/+Llbkpi55vXe6TrhWHu6t3y39kT0Z2BtuubmP8tvEErQjii8NfYG9hz/S209n29DY+6vARAdUDKo3pQwU2/qzsLKbsnsKeiD283/Z9evn2KnIb5ibmDG84nMM3DnMy5mQpqDQibF2g/7cweDHEXdIykB5bqp8kdHrExc6S74cHcDM5ndd+PUKGnid74++kM+Lnf0lKzWTR863xcq5Yq3J1xcnGHCvz8r2IKyw+jC+CvyBoRRBvbH+DEzEnGNF4BGufXMuS3ksYVG9Q3vqdyoZOxi+E6CWEOCeECBVCTCngdUchxHohxHEhxCkhxOh8r10WQpwUQhwTQgTrU3xhSCmZcWAGm65sYmLARAbVG1TstgbVG4S9uX3F7vXnp9ET8OpebS3Cmldh1YuQmmBoVffQ1NORTwc1499LcXz452m9tZuSnsULi4K5evMO80cE0NC9CBv3VDCEEHg4WhNVzow/KT2JledXMuzvYfRf258lp5fQ3LU5X3f7ms1Pb2ZCywnUctQhJUoF55EDl0IIU+BboDsQDhwSQqyTUub/HzcWOC2l7CeEcAXOCSF+lVLmxgp2lVKWSeC4lJKZwTP548IfvNzsZUY2Hlmi9mzNbRlcfzA/h/zM1dtX8Xbw1pNSI8bRE0auh91fwo7/Qfi/MOgno9p68skWNTgVmcAPuy/R2MOBIa2K97kkpWUSfDmOA2FxbDt7gwvRSXz7rD9ta5efTIulhbuTFZHlKF/P0jNLmXV4FqlZqdR2rM3EgIn0rdW3XGXNLCt0mbFqDYRKKcMAhBDLgP5AfuOXgL3QBsnsgDggU89adWLeiXksPr2YYQ2HMbb5WL20OazhMH45/Qu/nP6FaW2m6aVNo8fEFDpPglqdYdUL8HMv6PI2dJxQ7P0O9M3kXg04G5XIu2tOUaeaPS19Hr0rWmJqBsFXbnEg7CYHwuIIiUggK1tiZiLw83LiqyHN6d20YqzOLCnVHazZG2r8C/1AW40/M3gmAW4BjG8xniYuTSrVmH1R0cX4awDX8j0PBx67r8w3wDogErAHhkgpcwdfJbBJCCGB76WU8ws6iRBiDDAGwNu7eL23JaeX8N2x7+hfuz9vtXpLbx+8q40rfWv1ZU3oGl5r/hrOVs56abdc4NVaW/n75wTY/n8Qth0GztfuCgyMmakJXw9twRPf7OXVJYdZ/3qHB0IPb6dmEHw5joNhcRwIu8nJiASyJZibCpp7OfFq59q0qVUVfx+nShe58yg8nKyITkwlMysbMyMOZc2W2UzfPx17c3s+6/TZPSlYFAWjyze9IPe8f8avJ3AM6AbUBjYLIXZLKW8D7aWUkUKIajnHz0opdz3QoPaDMB8gICCgyDOK8anxzD0+l+4+3Znebrre4+5HNR7F6tDVLDu7jNeav6bXto0eK0cY9KMW6//3RJjbPmcD+ycMrQwnGwt+GBHAgO/28vLiw/w4MoDj1+I5eEkz+pB8Rt/Cqwpju9bRjN67CtYWxnHnYqy4O1qTLeFGYho1cjZnMUZ+P/c7x2OO83GHj5Xp64guxh8O5F+z7InWs8/PaOATKaUEQoUQl4AGwL9SykgAKWW0EGI12tDRA8ZfUpysnFjSewk17GqUyuKLWk616OLZhd/O/sboJqOxNtP/f4TQW6HYWdjplg66rBFCC/f0aq0N/SwfDv4jtT0ILGwNKq1+dXu+HOzHK0uOEPB/WwCwMDWhubcT47rVpY2vMy2U0ReZ/Hn5jdX4o5Kj+OrwV7T3aE/fWn0NLafcoItDHgLqCiF8gQjgGeDZ+8pcBQKB3UIIN6A+ECaEsAVMpJSJOX/3AGboTf19lPYGx6OajGLUP6NYG7qWZxo8o9e2t13dxn93/hcpJd19ujOi0QiaujbV6zn0QtXa8Pwmbdhn72y4ul+b+HVvZlBZvZq4M/OpZoTfSqFNraq08HbCylwZfUnIvxNXSx8DiykAKSUfHfgIiWRam2lqTL8IPHI8REqZCYwDNgJngOVSylNCiFeEEK/kFPsQaCeEOAlsBSbnRPG4AXuEEMeBf4G/pJT/lMYbKQv8q/nTzKUZi04tIis7S2/tbr2ylf/u+C+NnBsxvNFw9kbs5dm/n2X438PZdHkTmdkGmScvHDML6D5DS/mceht+DIT93xk85v/pAC/e7F6PtrWrKtPXA9Udc1fvGmdkz6Yrm9gRvoOxzcfiaW/4OafyhJBGtkAHtDH+4OAyCfkvMpuvbGbCjgl83vlzetbsWeL2tlzZwqSdk2jk0oh5QfOwt7AnOSOZNaFrWHJ6CeFJ4dSwq8GzDZ5lYN2BxrfgJDkW1o6D8xugTnfo95VRTPwqSo6Ukibvb+TpAC+mP/Ho/FZlSUJaAv3X9MfN1o1fe/9a4XPr6IIQ4rCUMkCXssY7VW+kdPPqhre9NwtDFlLSH81NlzcxcedEGrs05vug77G3sAe0tQPDGg7jzwF/8lWXr3CzcWNm8EyCVgYx89BMIpIi9PFW9IOtCwz9Tcv3f2kXzGoMPwZpw0BGmPZBoTtCCNydrI2yxz/r8Czi0+L5oN0HyvSLgTL+ImJqYsqIRiMIuRlC8I3i35VsvLyRt3a9RTPXZnzf/fsCe/KmJqYE+gSy6PFFLOuzjM6enVl6Zim9/+jNf3f8l2PRx0rwTvSIEFq+/7EHoOs0yEqHze/B1/7wXTvY/j+IOmnwoSBF0XF3tDK61buHog6x6sIqRjQeQQPnBoaWUy5RQz3FIDUzlR4re9DUtSnfBn5b5Pr/XPqHKbun4Ofqx3dB32FrrntUTFRyFL+d/Y0V51eQmJ5IM9dmDG80nCDvIOPq+dy6Amf/gjPrtQlgJFSpqe372/AJbV9iE9XvMHYmrzzBtnPRHHonyNBSAO3/3lPrnyIrO4s/+v9RKtF15RU11FPKWJlZMbThUHaF7+JifNGGM/4O+5vJuyfj5+rH3KC5RTJ9gOq21Xmz5ZtseWoLUx+bSnxqPJN2TqL3H71ZdGpR0fYJLk2q+EDb1+D5DTDxPPSbDVXrwMHvteyfXzbQtoC8uA2yMgytVlEI1R2tiE1KIz2z7Hc+K4j5J+Zz5fYV3mv7njL9EqB6/MXkVuoteqzsQS/fXnzY/kOd6vwZ9ifv7HkH/2r+fBv4LTbmJc/8mC2z2XltJ4vPLOZQ1CEsTS3p4dODgXUHGmd+8dQEbcevs+vhwmbIuKMtEKv3ODTsC7W7GXxdgOIuvx+6yuRVJ9n9VleDZyo9F3eOZ/58ht61evNRh48MqsUYKUqP34jGBsoXVayq8GSdJ1l5YSWvt3i9wE3b87P+4nqm7Z1GS7eWfNPtG72YPoCJMKGrd1e6enflbNxZVp5fyd9hf7M+bD0+Dj4MrDuQJ2o/gYu1i17OV2KsHKHZ09ojIwUubteGg879DSeWgYk5eD0GtbpoD48W2p7BCoPgni8vvyGNPys7iw/2f4CDpQOTAiYZTEdFQfX4S8C1xGv0Xd2XkY1HMqHlhELLrQ1dy7t736V19dZ8Hfh1qd+ipmSmsOXKFlZdWMXhG4cxE2Z08uzEoHqDaOfRzrjmAnLJyoAreyF0K1zaCddPABIsHaBmRy1ZXK0u4FJPm0xWlAmh0YkEfbmLr4Y058kWpb/dZWEsOb2ETw99yqcdP6V3rd4G02HMqB5/GeFl70WQdxArzq1gTNMxBUbmrAldw3t736O1e2u+7lb6pg9gbWZNv9r96Fe7H5cSLrE6dDVrQ9ey7do2qtlU48k6TzKgzgDjWvRian63lw+QfBMu74KwHdrjXM6m9/bud8v5dgYHlUmzNMnt8RtyQ5bIpEjmHJ1DhxodeNz3cYPpqEioHn8JCYkNYehfQ5kYMPGB3P+rL6zm/X3v08a9DXO6zcHKzHCbVmdkZ7Dr2i5WXVjF3si9ZMts2ri3YVDdQXTz7oaFqZFvLxh3SbsTCNup/XvnpnbctcHdHwKf9mBVeTdPKS2aTd/Iky1qMKN/kzI/t5SSsVvHEnwjmDX91+Bh51HmGsoLqsdfhjRxaUKAWwCLTy/m2YbPYm5iDsCq86uYvn867TzaMbvrbIOaPmjbSAb6BBLoE0hUchRrQtew+sJqJu2ahKOlI/1q9WNg3YHUrVLXoDoLxdlXe7QcBdnZcCPk7t3A4UVwcB4IU6jhr90J+HbS5grMy/C6Z2eBMKlwQ1HujtZExhumx//P5X/YHbGbt1q9pUxfj6gevx7YFb6LsVvH8nGHj+lXux8rzq9gxv4ZtK/RntldZ2NpamloiQWSLbM5cP0Af1z4g61Xt5KZncmoxqP4b8B/DS2taGSmQfghbaL40i6IOAwyC8ysNPP37aTdEbg3199EsZQQF6adK+IIRB7R5iUcPaH3TKjdVT/nMQJGLfiXmMQ0/hrfsUzPG58aT/+1/fGw9WBJ7yWYGskGQMaK6vGXMR1rdKSOUx0WnFpASmYKHx74kI41OjKr6yyjNX3QIoLaebSjnUc74lLj+CL4CxaeWkigdyDNqzU3tDzdMbOEmh20B2iJ467uzxkW2gXbPtQelg7acJBvJ22y2LWh7ovIbkfea/KRR+/uRWxmre1R3HIkXNgEi5+ExgOh58cVYg7C3dGaE+Flv+/y58Gfk5CWwPzu85Xp6xnV49cTa0LX8O7edwHo5NmJWV1mGf+4+X3cybhD/7X9sTO3Y3m/5XnDVuWe5FjtB+DSLm1+IC5MO27jov0I5P4QVPHVhmnuxGnmHnE0598jkBSl1TExg2qNoEZLbVjJw1+bZ8i9k8hI1fIU7f4CTC2g2zvQ6qVyHZL69dYLfLH5PGc/7FVmWU8PXD/AS5te4sWmL/KG/xtlcs7yTlF6/Mr49URGVgaD1g+itmNtPu30abkz/Vx2XNvB69te5w3/N3ix6YuGllM6xF+794cg8bp23NFLM/Zbl+6WrVr3XpOv3gTMdYjMiguDvydB6Bao3hT6zAKvVqXzfkqZlYfDmbjiODsmdqGmS+kvrkvNTGXguoEIBKueWGXw+bHyghrqMQDmpuasfmJ1ub8l7eLVhe4+3Zl3fB49fXri5eD16ErlDScvaDFMe0gJN0O1SeLLu7XnLUdqJu/RXFtwVhyca8GwlXBmHWyYAj8FaTuWBU0Hm/K1Z7N7Tl7+yISUMjH+ucfnci3xGj/1+EmZfimhjF+PlHfTz2Vyq8nsi9zH/x38P+YFzTO+tA/6RAhwqas9Wr+k/7Yb9dfSUOz8VNus5uyf2iY2fs+WmyR1ucZ/vQwie87GnWXRqUUMqDOA1u6tS/18lZXy8c1TlClutm6MbzGefZH72HBpg6HllH8s7aHH/8Eru7WVx2vHwoLHISrE0Mp04u4irtLNy5+Zncn0fdNxtHQsf5Fl5Qxl/IoCGVJ/CE1dmvLpoU9JSCv7iI4KiVtjGPU39P8Obl6A7zvBxncgLdHQyh6KtYUpVWzMS2317s2Um2y9spV39rzDqZuneLv12zhaFnOITaETaqhHUSCmJqa83/Z9hvw5hFmHZzG93XRDS6oYmJhocwv1H4etM2D/txDyB/T6nzYsZKTDau6O1noxfiklEUkRHIk+wpEbRzh84zCXb18GwMLEgiH1h+hlS1PFw1HGryiU+s71GdFoBAtOLaBf7X60dGtpaEkVBxtnbX/i5sPgrwmwYiTUDoQ+n2sTw0aGu6MVEfFFH+rJltlcuHWBI9FHOHrjKIejDxN9JxoAewt7WlRrwZN1nsTfzZ/GVRuXr2g4KbVQ4bgw7XHrkjasF/C80acWV+GciodyJ+MOA9YOwMrMipX9VmJuWkFi+42JrEwI/gm2/Z9mJgO/hwZ9DK3qHqatOcn649c5/n6Ph5bLyMrg1M1THL5xWDP76KMkpmtDWdWsq+Hv5q89qvlTt0pdTISRjzZnZ2trOHLNPe9xSXuk5xumEyYgs7VEgt2mgd9QKMOADxXOqdAbNuY2vNPmHcZuHcuCUwsY02yMoSVVPEzN4LGXNbP//TlY9ix0ngydpxhN5I+7ozUJKRncSc/ExqJg21gYspBvjn1DWlYaADUdatLDp0ee0dewq2G8EWLZWXD9GEQeu2vst3LMPTPfnY6JmbaFaBVf8G6r3Z3lPpy8tQV/G9/RJvAPzoMeH2mLA40MnYxfCNELmA2YAj9KKT+573VHYAngndPm51LKBbrUVRg/nTw70bNmT74//j09a/bEx8HH0JIe4HrSdWYcmIGjpSPTHptWYIpso8fRE0b/ow397PwUrh+HAd+DtZOhleHhlBPSmZBKbdeCr+3mq5txt3XnDf83aFGtBVWtq5alxKITf1XL73Rxm7aQL+WWdtzMSjN351paKK6z711zd/B8+Cps7zbw4hYIWQVbPoBfnoB6vaD7h+Bar0zeli480viFEKbAt0B3IBw4JIRYJ6U8na/YWOC0lLKfEMIVOCeE+BXI0qGuohwwudVk9kbs5cMDH/JD9x+Mque24dIGPtz/IZkyk/SsdEJiQ/iyy5fUq2I8/9F0xtwK+n+r7Tz2zxT4oRs8sxSqNTCorLyQzvjCjT8yKZJOnp0I8jGOjdkfIPU2XN6jGX3Ydm3hHmhDM/Ue10zeuw041CjZnZYQ0PQpaNAXDs6F3V/Cd20gYDR0eRtsDb8bni7vrjUQKqUMk1KmA8uA/veVkYC90NzADogDMnWsqygHuNq48h///3Dw+kH+DPvT0HIASEpPYuruqby16y18nXxZ1W8VP/X8iTsZdxj21zDWXVxnaInFQwhtMdnI9ZB2G34M1LanNCAeuVswFhLLn5qZSmxKLB62RpQ6OSsTrh2CHZ/Cz73gM19YNhSO/ar13nv+D147CBPOwIC52nagTl76G14zt4IOb8L4o5rpBy+AOS1gz1daTicDostQTw3gWr7n4cBj95X5BlgHRAL2wBApZbYQQpe6AAghxgBjALy9vXUSryhbnq7/NOvC1jHz0Ew61uiIk5WTwbQcjT7K27vf5nrydV71e5UxzcZgZmKGl4MXy/st561db/HOnnc4Gn2UKa2nGHWW1ELxaQdjdsLy4drYf6dJWo/RACvE3Ry161fY6t3I5EgAatgbbntGQBuTz+3Rh+2CtARAaOk32o3XevVerbWMrmWFrQv0+QJaj4HN78GW97XJ/MD3ockgg4Tw6vLTVpCq+0OBegLHAA+gOfCNEMJBx7raQSnnSykDpJQBrq6uOshSlDUmwoT3275PYnoiXx7+0iAaMrIz+Pro14z6ZxQAi3ot4rXmr92zj7CLtQvzu8/nxaYvsvL8Sob/PZzwxHCD6C0xjjW0RV8tnoNdM+G3ZyAlvsxlWJqZ4mJnUejq3YjECABq2BnI+DPTYfkImNNcmyOJPAaN+8NTC+CtMBizA4LeB9+OZWv6+XGtD8/+DiPWgqUjrHoBfuoO1/4tcym6GH84kD9Tlydazz4/o4E/pEYocAlooGNdRTmiXpV6jGw8ktWhqzkUdahMz3319lVGbhjJ/BPz6VurLyv7rSx03wAzEzPe8H+Dr7t9TXhSOIP/HMyOazvKUq7+MLeCJ76BPl9qk5E/dIXoM2Uuw93RmshCFnFFJuX0+A1h/Jnp2jqI02u1u6Jxh+E/J+GJr6HJQONLilerC7y8U5vLib+mmf/ykdrdShmhi/EfAuoKIXyFEBbAM2jDOvm5CgQCCCHcgPpAmI51FeWMl/1epoZdDWbsn0F6Vnqpn09KyeoLq3lq/VNcvn2ZmZ1n8lGHj3SK3Oni1YXf+/6Op50nr297na8Of0Vmdmapa9Y7QkCrF2DUn5CWBD8EakZXhrg7WhFVWI8/KQILEwtcrMt44jIzTevpn/sben+uxc+71DHaFdB5mJhqd3Hjj2jDdxc2wbettVDQMhj/f6TxSykzgXHARuAMsFxKeUoI8YoQ4pWcYh8C7YQQJ4GtwGQpZWxhdUvjjSjKDmsza95t8y6Xb1/mp5CfSvVc8anx/Hfnf3lv33s0cWnCH0/8Qa+avYrUhpe9F4t7L+apek/xU8hPvLz5ZWJTYktJcSnj3UbrLbo10gxvywdaDHoZ4OFkXegYf0RSBB52HmW7ICvX9M9vyBlD13N21bLAwha6TIHXj0DTwXBln7aBT2kjpTS6R8uWLaXC+Jm0c5Js8UsLGRYfVirt74vYJ7v93k02/6W5/PnkzzIrO6vEba4NXSsDFgfIrr93lcFRwXpQaSAyUqVc+7qU7ztIuXiglHfiSv2Uc3eESp/Jf8rbKekPvDZk/RD58qaXS11DHhmpUi55Wnv///5YductbTJSi10VCJY6eqxxLAtUlEveavUWVmZWfHjgQ6QeU3+kZ6Xz+aHPGbN5DDbmNvza+1dGNxmtl97kE7Wf4Nc+v2JjbsMLG19gYchCvWovM8ws4Yk50PcrbW/h+V3gRuneTOfl5S9gnD+3x18mZKbB78PhwkboO0sbAqsolNHEszJ+RbFxsXbhzZZvcijqkN5i5kNvhfLsX8+y6PQihtQfwvJ+y2lUtZFe2s6lXpV6LOuzjG7e3fji8Bf8Z/t/8vLJlDsCRsPov7Vx4R+DSjXe38MpJ5b/vmRtyRnJxKfFl83EbkaqFtp6YaP2oxfwfOmfswKijF9RIgbVHUSLai34PPhzbqXeKlJdKSUxd2I4cuMI6y6u48vgL3nmr2eISYnhm27fMK3NNKzNdNjfthjYWdjxRecvmBQwiV3huxjy5xDOxZ0rlXOVOl6ttXDFao1gxWi4sr9UTpPb44+6r8cfkVRGoZx5pr8px/RHl+75KjAqSZuiRJgIE95r8x5P//k0nwd/zkcdPrrn9fSsdMKTwglPDOda4jXCE7W/c4+lZqXe01Ynz0683/b9MokOEUIwovEImro2ZeKOiQz7exhTWk9hUN1BRpWSQicc3OG5lVq0z+/D4KXtUEW/OZXcHKwQggdCOssklDMjVXtfoVug32xoOar0zlUJUMavKDF1qtRhdOPR/HDyB5wsnbidfjvP6KPvRCPzrdmzNrPG094TL3sv2nm0y/vby94LD1sPg6R9blGtBcv7LWfK7il8sP8DdobvZHrb6cafZOx+rKtoC4R+DNQWer2wScsPryfMTU1wtbPk+n1DPbk9/lIb47/H9OdAy5Glc55KhDJ+hV4Y02wMm69s5pfTv+Bi7YKXvRetq7fGy94rz9w97T2palXVKHvTVa2r8n3371lyegmzj8xm4LqBTG87na7eXQ0trWi41IWnF8KSp2DVS/DMr3pN8eDu9OBOXBFJEVibWeNsVQoLpTJStTTVF7dpC7L8R+j/HJUQZfwKvWBlZsXKJ1aSlZ2FjbmNoeUUCxNhwojGI2jn0Y6397zN+O3jGVh3IG+1egtbc+PeUekeaneDXp/Ahkna9o7dP9Bb0x6OVpy7ce9EeERiBB62Hvr/Qc9IyTH97TmmP1y/7Vdi1OSuQm9YmlqWW9PPT50qdVjaeykvNn2RNaFrGLRuEEduHDG0rKLR+iVoORr2fgXHftNbs+6O1kQlpN4TAhuZHKn/5GzK9EsVZfwKRQGYm5rzhv8bLOy1EIFg1D+jmHV4VpmkqNALQkDvmVCzI6wfr7dEYB5OVtxJz+J2yt20F7k9fr2RkQK/DdVMv/83yvRLAWX8CsVDaFGtBSufWMnAugP5OeRnnv3rWS7cumBoWbphag6Df9E2Fln2rJYQrIS435eX/3b6bRIzEvG09yxx28Bd0w/boSUxa/GcftpV3IMyfoXiEdia2zK93XS+7vY1MSkxDPlzCItOLSJbZhta2qOxcdYifTLTNENNSypRc9XzVu9qxp+bjlkvET3pd7RopLAd8OR30GJYydtUFIgyfoVCR7p4dWF1/9V0rNGRz4M/54WNL+TFsBs1rvW1vPTRp2D1y5Bd/B+s3L13I3OStekthj8rQ9twJmynZvrNny1Ze4qHooxfoSgCzlbOfNX1K2a0m8GZuDMMWjeIdRfXGX++n7pB0OMjOPsnbP/o0eULoZq9FaYmIq/HH56kbXBTIuOXEta9fndxljL9UkcZv0JRRIQQDKg7gJX9VlKvSj3e2fMOE3ZMKHLKijKnzataHPzuz+HEimI1YWoicLO3zIvlj0iKwM7cDgcLh+Lr2vYhHP8NukxVi7PKCGX8CkUx8bT35OeeP/NmyzfZEb6DAWsH8O/1st9GT2eEgN5fgE97WDsWwg8Xqxn3fHn5I5MiqWFXo/gx/P/+ALu/AP+R0Pmt4rWhKDLK+BWKEmBqYsrzTZ5nWZ9l2FvYM2nXJO5k3DG0rMIxs4DBi8G+OiwbCgkRRW6iuqPV3cndkqRjPrMe/p4E9R7XtpU0whXdFRVl/AqFHqjvXJ8P239IXGocS88uNbSch2NbVYv0Sb+jmX96cpGqezhacT0hlezsbCKSIoo3vn/1AKx6EWq0hKd+BlOVRKAsUcavUOiJ5tWa08WzCz+H/ExCWoKh5Tycag3hqZ/g+glY82qRIn3cHa1Jy8zm0q1oUjJTim78Medg6RBtfcGzy8Gi/K/2Lm8o41co9Mi4FuNITE9k4amFhpbyaOr1hB4fapu27/xU52q5IZ0hNy4DRYzouX0dlgzS9pUd/od296Eoc5TxKxR6pL5zfR73fZxfz/xaPjZ0bzsOmj8HOz+BkD90qpK7evd83BWgCIu3UhPg16cg5RYMWwFVahZHsUIPKONXKPTM2OZjSc9K54cTPxhayqMRAvp+Cd5ttSGfiEdH+uTuxHU5oQgx/Jlp2u5ZMWe1NBIezUuiWlFCdDJ+IUQvIcQ5IUSoEGJKAa9PEkIcy3mECCGyhBDOOa9dFkKczHktWN9vQKEwNnwcfBhQdwDLzy8vHyt7zSy1SB/bavBzLy3S5vb1Qou72Flibiq4nhyBo6UjdhZ2D28/OxvWvAaXdmn5d+oE6vkNKIrKI41fCGEKfAs8DjQChgoh7tn9Wko5U0rZXErZHHgb2CmljMtXpGvO6wH6k65QGC8vN3sZE0yYe3yuoaXohp0rvLAR/IZC8M8wpzn8MxWSoh8oamIicHOw4mbqDd16+1veg5CVEPg++D2jf+2KIqNLj781ECqlDJNSpgPLgP4PKT8U0F8CcIWiHFLdtjrPNHiGdRfXEZYQZmg5uuHgAU/MgXHB0OQpODgPvmoGm96F5Jv3FPVwtCYpSwfj3/8d7PsaWo+BDm+WonhFUdDF+GsA+fO5huccewAhhA3QC1iV77AENgkhDgshxhR2EiHEGCFEsBAiOCYmRgdZCoVx80LTF7AyteKbo98YWkrRcPaFJ7+FcYeg0ROacc9upu3mdUe7ka/uaEm6iHu48Yf8ARunQsN+2o5gaoGW0aCL8Rf0aRWWkaofsPe+YZ72Ukp/tKGisUKITgVVlFLOl1IGSCkDXF1ddZClUBg3zlbOjGg8gs1XNnPq5ilDyyk6VWvDwPkw9iDU7QG7v4TZfrD9f7jZ3ASRUfgGLJd2a5lAvdvAwB/0uu+vouToYvzhgFe+555AYTNWz3DfMI+UMjLn32hgNdrQkUJRKRjRaASOlo58ffRrQ0spPq714ekF8OpeqNUZdn5C93MvAmAnC5jYvXEKlg2DKr7wzFIwty5jwbqRkp5FSEQCa49F8MWmc7z262H+s+wouy/EkJ1t5NlWS4gu66QPAXWFEL5ABJq5P5A3VQjhCHQGnst3zBYwkVIm5vzdA5ihD+EKRXnA3sKeF5q8wJeHvyQ4KpiA6uU4vsGtMQxZAtePc/Gv/wI3qP/XeIgLhVYvgaUdJITDkqe01bjPrdI2gjEw8XfSCY1OuvuI0f6NiE8hN5u2iQBvZxviUzJYcywSL2drnmnlzdMtPanmYGXYN1AKCF3yiAshegNfAabAz1LKj4QQrwBIKefllBkF9JJSPpOvXi20Xj5oPzJLpZSPTAYeEBAgg4NV5KeiYpCamUqfP/rgae+p7eFbAca6P9wzh+UXf+DvFG+8ovaAjQu0ex2OL4PbETB6A1RvUqaaktIyOXY1ntDoxDxzD41OJjYpLa+MhZkJtVxsqVPN7p5Hzaq2WJmbkpqRxcZTUfz271UOhMVhaiIIbFCNoa296VTPFVMT4/3shBCHdY2c1Mn4yxpl/IqKxvJzy/nwwId8F/gdHT07GlpOiZmy813Wh25hYoOljPaOge0fQ9h2MDHXUjH4FjiVVyqERifyy/4rrDocTnJ6FgD2Vmaaqbvea/CeVWx0Nu+wmCR+P3SNlYfDuZmcjoejFYNbeTE4wAsPJ+MbvlLGr1AYGRlZGTyx5gnsLOz4ve/vmIjyvWj+xY0vsv/SdYZ5f87U3g21g1cPapE7XqU/jZeVLdl65ga/7L/CntBYLExN6OvnzpPNa9Cguj2u9pZ6u7NKz8xmy5kb/PbvVXZfiMVEQJf61XimlRfdGlTDzNQ4PsuiGL/KhapQlAHmpua81vw1pu6ZyqYrm+hVs5ehJZWIyORIrIQrkfEpdw96P1bq572VnM7vwddYvP8KEfEpuDtaMalnfYa08sLFzrJUzmlhZkLvpu70burOtbg7/H7oGsuDrzHmbDRuDpY83dKLIa288HIuP1lGVY9foSgjsrKzeGr9U2RmZ7K6/2rMTMpnvysrO4uAXwNwSg/COX0Aq15tV+rnDIlI4Jf9l1l7LJK0zGza1HJmZNuadG/kZpAed2ZWNtvORrPs0DV2nItGAh3quPBq59q0q+NS5npA9fgVCqPE1MSUcS3G8Z/t/2H9xfUMqDvA0JKKRUxKDJnZmbhauRMenfLoCsUkIyubDSFR/LLvMsFXbmFtbsqglp6MaOtDg+ol2ONXD5iZmtCjcXV6NK5OZHwKK4LDWXboKqMXHmLnpK5UdzTuSCBl/ApFGdLNqxtNXZry3fHv6FOrDxamFoaWVGTCE3OyctrX4HBiGlnZUq/RLtGJqSw9eJWlB68SnZiGT1UbpvVpyNMBXjham+vtPPrCw8maN4LqMtC/Bt2+2MHsrRf438Cmhpb1UIxjVkKhqCQIIRjvP56o5ChWnF9haDnFIjJZW7/p6+RFVrYkJjHtETV0IywmifG/HaX9J9v4assFGnk4sGBUK7b/twsvdqxllKafHy9nG55t7c3y4Gtcji3adpZljTJ+haKMaePehseqP8b8E/ONe2P2QohIjEAgaOCiLeiPTCj5cE92tuSVJYfZdjaa4W1qsn1iFxaObk3XBtUwMeLY+fsZ260OFqYmfLn5vKGlPBRl/AqFAXjd/3XiUuNYcmaJoaUUmYikCFxtXPF0cgTgenxqidv8O+Q6528k8fHAprzXrxG+LrYlbtMQVLO3YnT7mqw7HsnpyNuGllMoyvgVCgPg5+pHF68uLAxZaPwbs99HRFIEnnaeeXvvXi9hjz87WzJn6wXqVLOjT1N3fUg0KC93qo2DlRmfbzpnaCmFooxfoTAQ45qPIykjiQUhCwwtpUhEJEXgYeeBo7U51uamXE8oWY9/Q0gU528kMT6wrlGnRNAVRxtzXulSm21nowm+HPfoCgZAGb9CYSDK3cbsQEZ2BjfuaBuwCCFwd7IqUY8/O1sye+t5arvaVojefi6j2/niam/JZ/+cwxjXSinjVygMyNjmY8nMzmT+ifmGlqITUclRZMvsvA1YPBytiSzBGP8/pypWbz8XawtTxnerw7+X49h53vg2llLGr1AYEG8HbwbUHcCK8yuISIowtJxHkrt5fK7xV3csfo8/O1sye8sFarva0rdZIRu6lGOGtPLGy9mamRvPGV1+f2X8CoWByd2Y/btj3xlayiPJ/XHysNOM2sPRiujENDKysovc1j+nojh3I7HC9fZzsTAz4c2gepyKvM3fIdcNLecelPErFAbGzdaNoQ2G8mfYn1yMv2hoOQ8lIikCU2FKddvqALg7WSMlRBdxEVduJE+tCtrbz6V/8xrUc7Pjy03nySzGj2NpoYxfoTACcjdm/yL4C6OcDMwlIikCNxu3vARz7jk5aa7HF224Z+OpKM5GJfJGBe3t52JqIpjYoz5hscmsPBxuaDl5KONXKIyAKlZVGNdiHLsjdhv1oq7IpEhq2NfIe567IUlkEUI6tUieit/bz6V7IzdaeDsxe+sFUjOyDC0HUMavUBgNzzV8ji5eXfjy8JecjDlpaDkFEpEYgYftXbOuXowe/6bTWm9/fLeK3dvPRQjBpJ71uZ6QypIDVwwtB1DGr1AYDUII/q/9/1HNuhoTd040uhW96VnpRKdE39Pjd7Ayx87STOdFXNnZkq+2XKCWiy39/Cp+bz+XdrVd6FjXhe92XCQpLdPQcpTxKxTGhKOlIzM7zyQ6JZppe6cZ1Xj//aGcubgXIaQzt7f/emCdStHbz8/EHvWJS07nx91hhpaijF+hMDaauTZjQssJ7Li2g8WnFxtaTh65oZwPGL+TtU49fm1sP1Tr7VeCsf378fNyolfj6vy4+xJxyekG1aKMX6EwQp5r+BzdvLox6/AsjsccN7QcoHDj93C00mn17qbTNzhz/TavB9Yxmg3Ky5qJPetxJz2TuTtCDapDp6svhOglhDgnhAgVQkwp4PVJQohjOY8QIUSWEMJZl7oKheJBhBDMaD8DN1s3Ju2cZBTj/RFJEZiZmOFq7XrPcXdHa2KT0kjLLDxiJTeSx7eS9vZzqVPNnoH+nizaf6XEWU1LwiONXwhhCnwLPA40AoYKIRrlLyOlnCmlbC6lbA68DeyUUsbpUlehUBSMo6Ujn3f+nJiUGKbtMfx4f2RSJB62HpiamN5zPDeW/0ZC4Yu4Np/J6e13q7y9/Vz+E1QXKbUFbIZCl0+gNRAqpQyTUqYDy4D+Dyk/FPitmHUVCkU+mrg0YWLARHaE7+CX078YVEtuOub7cc/Jy1/YTlxSajl5fF1seaISRfIUhmcVG4Y95sPy4HDCYpIMokEX468BXMv3PDzn2AMIIWyAXsCqYtQdI4QIFkIEx8QYXzY7hcJQPNvgWYK8g/jq8Fcciz5mMB0RSREPjO+DNtQDEFXIBO+m0zc4rXr79zC2ax0szQy3RaMun0JBMVeF3XP2A/ZKKXN3H9C5rpRyvpQyQEoZ4OrqWlARhaJSIoTgg/YfaOP9uwwz3n8n4w5xqXEFGr/HQ3r8ub39mlVtVG8/H672ljzf3pc/T1znVGTZf566GH844JXvuScQWUjZZ7g7zFPUugqFohAcLBz4ovMXxKbE8s6ed8iWZZvw63qyll2yIOO3sTDD0dq8wL13N+f19uuq3v59vNSpFo7W5ny+sey3aNTlkzgE1BVC+AohLNDMfd39hYQQjkBnYG1R6yoUikfT2KUxEwMmsjN8J7+cKtvx/vvTMd9PQYu4pNQieWpWtaF/c9Xbvx9Ha3Ne7VKb7ediOFTGWzQ+0villJnAOGAjcAZYLqU8JYR4RQjxSr6iA4BNUsrkR9XV5xtQKCoTzzZ4lu4+3fnqSNmO9+cav6e9Z4GvuxcQy7/lTDSnIm8zTvX2C2Vk25pUs7fks3/OlmnUlk6fhpTybyllPSllbSnlRznH5kkp5+Urs1BK+YwudRUKRfEQQvBBuw9wt3Vn0q5JxKfGl8l5IxIjsDS1pKpV1QJfd3eyJur2XeOXUvLVlvP4VLXhSdXbLxRrC1NeD6zLocu32HGu7IJa1M+wQlHOsLew5/Mun3Mz5Sbv7C2b8f7I5Eg87DwQouD8Oh6OVsQlp+elHc7t7aux/UczJMALb2ebMt2iUX0iCkU5pHHVxkxqNYld4btYdGpRqZ8vPDG80PF9uBvSeT0hNWdsX/X2dcXCzIQJ3etx+vpt/jpZNls0KuNXKMopz9R/hh4+PZh9ZDZHo4+W6rkikiLwtCt4fB/u3Ylr65loQiJuM66ritvXlSf8PGhQ3Z4vN58v1v7FRUV9KgpFOUUIwfR20/Gw82DSzkncSr1VKudJTE/kdvrtAkM5c3HP2YkrIj6Fr3J6+wNaFF5ecS8mJoKpvRsyql3NsjlfmZxFoVCUCvYW9nzR+QviUuOYumdqqYz35+bhf/hQj9bj/+3fq4RE3Gas6u0XmU71XBnZribmZXDd1CejUJRzGlZtyORWk9kTsadUxvvzQjkfMtRjZW6Ks60FR67G4+2sevvGjjJ+haICMLj+YDrU6MDCUwvJytbvht6PWryVS26vf1y3OmXSa1UUH/XpKBQVACEE/ev0Jy41Tu8TvZFJkdiY2eBk6fTQcnWq2eHrYqt6++UAM0MLUCgU+qFTjU5YmFiw5eoWAqoH6K3d8KTwh8bw5/LxgKZkZknV2y8HqE9Ioagg2Jjb0K5GO7Zc2aLX5f+RSZEPHd/PxdbSDEcbc72dV1F6lJsef0ZGBuHh4aSmPnpvT0XFxcrKCk9PT8zNlcEURHef7uy4toOQ2BCaujYtcXtSSiKSIghw098dhMLwlBvjDw8Px97enpo1az7yllNRMZFScvPmTcLDw/H19TW0HKOks2dnzIQZW65u0Yvx306/TXJG8kNj+BXlj3Iz1JOamkrVqlWV6VdihBBUrVpV3fU9BEdLR1pVb6W34Z7wpHCg4Dz8ivJLuTF+QJm+Qn0HdCDIJ4iriVe5EF/yzbxzF2/VsFfGX5EoV8avUCgeTTfvbggEW69sLXFbEYm6xfAryhfK+BWKCoaLtQstqrVg89XNJW4rPCkcewt7HCwc9KBMYSwo4y8DduzYQd++fQ0tQ+9Mnz6dzz//HIBRo0axcuVKAytS5BLkE8SFWxe4cvtKidrRNZRTUb5Qxl8Oycoq/pL8ktRVlB+CvIMA2Hq1ZMM9EUkRapinAlJuwjnz88H6U5yOvK3XNht5OPB+v8aFvn758mV69epFhw4dOHDgAH5+fowePZr333+f6Ohofv31VwD+85//kJKSgrW1NQsWLKB+/fr3tNO0aVN2796No6MjLi4uzJo1ixEjRjB8+HBGjhxJnTp1GD58OMnJ2tbF33zzDe3atWPHjh188MEHuLu7c+zYMU6ePMmUKVPYsWMHaWlpjB07lpdffrlA7UWp+9lnn7F48WJMTEx4/PHH+eSTT/jhhx+YP38+6enp1KlTh8WLF2NjY6OPy64oJdzt3GlctTFbrmzh+SbPF6sNKSWRSZF0qNFBz+oUhqZcGr+hCA0NZcWKFcyfP59WrVqxdOlS9uzZw7p16/j444/55Zdf2LVrF2ZmZmzZsoWpU6eyatWqe9po3749e/fuxcfHh1q1arF7925GjBjBgQMHmDt3LiYmJmzevBkrKysuXLjA0KFDCQ4OBuDff/8lJCQEX19f5s+fj6OjI4cOHSItLY327dvTo0ePQuPbdal79uxZ1qxZw8GDB7GxsSEuLg6AgQMH8tJLLwEwbdo0fvrpJ15//fVSvNIKfRDkE8TsI7OJSo6ium31Ite/mXqT1KxU1eOvgJRL439Yz7w08fX1pWlTbVFM48aNCQwMRAhB06ZNuXz5MgkJCYwcOZILFy4ghCAjI+OBNjp27MiuXbvw8fHh1VdfZf78+URERODs7IydnR0JCQmMGzeOY8eOYWpqyvnz5/Pqtm7dOs/YN23axIkTJ/LG1RMSErhw4UKhxq9L3S1btjB69Oi83ryzszMAISEhTJs2jfj4eJKSkujZs6c+LqeilAny1ox/69WtDGs4rMj1c0M51Rh/xUON8RcBS0vLvL9NTEzynpuYmJCZmcm7775L165dCQkJYf369QUuNOrUqRO7d+9m9+7ddOnSBVdXV1auXEnHjh0BmDVrFm5ubhw/fpzg4GDS09Pz6tra2ub9LaXk66+/5tixYxw7doxLly7Ro0ePQrXrUldKWWCc/KhRo/jmm284efIk77//vlpAVU6o6ViTOk512HJlS7Hq65qOWVH+0Mn4hRC9hBDnhBChQogphZTpIoQ4JoQ4JYTYme/4ZSHEyZzXgvUl3BhJSEigRg1tocvChQsLLOPl5UVsbCwXLlygVq1adOjQgc8//zzP+BMSEnB3d8fExITFixcXOhnbs2dP5s6dm3dXcf78+bx5gUdRWN0ePXrw888/c+fOHYC8oZ7ExETc3d3JyMjIm8tQlA+CfII4En2Emyk3i1w31/jVqt2KxyONXwhhCnwLPA40AoYKIRrdV8YJ+A54QkrZGHj6vma6SimbSykrdKant956i7fffpv27ds/NHrmscceo169eoA29BMREUGHDtoE2muvvcaiRYto06YN58+fv6ennp8XX3yRRo0a4e/vT5MmTXj55ZfJzMzUSWdhdXv16sUTTzxBQEAAzZs3zwvV/PDDD3nsscfo3r07DRo0KMolURiYIO8gsmU2269tL3LdiKQIqlhWwcZcTeRXNMSj8nkIIdoC06WUPXOevw0gpfxfvjKvAR5SymkF1L8MBEgpY3UVFRAQIHMnNHM5c+YMDRs21LUJRQVGfRd0R0pJ7z964+Pow7ygeUWq+/Lml7mddpvf+v5WSuoU+kQIcVjXzrUuQz01gGv5nofnHMtPPaCKEGKHEOKwEGJEvtcksCnn+JiHiB4jhAgWQgTHxMTool2hUDwCIQTdfbpz8PpBbqcXLQRaxfBXXHQx/oKyYt1/m2AGtAT6AD2Bd4UQ9XJeay+l9EcbKhorhOhU0EmklPOllAFSygBXV1fd1Cvu4eTJkzRv3vyex2OPPWZoWQoDE+gTSGZ2Jjuv7Xx04RyyZTaRSZEqOVsFRZdwznDAK99zTyCygDKxUspkIFkIsQvwA85LKSMBpJTRQojVQGtgV4mVKx6gadOmHDt2zNAyFEZGU5emVLOpxpYrW+hXu59OdWLuxJCRnUENW2X8FRFdevyHgLpCCF8hhAXwDLDuvjJrgY5CCDMhhA3wGHBGCGErhLAHEELYAj2AEP3JVygUj8JEmBDoHcjeyL3cybijU528iB7V46+QPNL4pZSZwDhgI3AGWC6lPCWEeEUI8UpOmTPAP8AJ4F/gRyllCOAG7BFCHM85/peU8p/SeSsKhaIwgryDSMtKY2/kXp3Kq1DOio1OK3ellH8Df993bN59z2cCM+87FoY25KNQKAyIv5s/VSyrsPnKZrr7dH9kebV4q2KjVu4qFJUAMxMzunp3ZVf4LtKz0h9ZPjIpEldrVyxNLR9ZVlH+UMavI5cvX6ZJkyY6lzdUfvqi6lRUHoK8g0jOSObA9QOPLKtCOSs25TJJGxumQNRJ/bZZvSk8/ol+26zkZGVlYWpqamgZihwec38MO3M7tlzZQifPAqOq84hIisDPVY3SVlRUj78IZGZmMnLkSJo1a8ZTTz3FnTt3mDFjBq1ataJJkyaMGTOGglZCF1amS5cuTJ48mdatW1OvXj12794NaIY5ceJEmjZtSrNmzfj6668BOHz4MJ07d6Zly5b07NmT69ev5x338/Ojbdu2fPvttw99D5cvX6Zjx474+/vj7+/Pvn378l777LPPaNq0KX5+fkyZoqVkCg0NJSgoCD8/P/z9/bl48eIDO4qNGzcuLzdRzZo1mTFjBh06dGDFihX88MMPtGrVCj8/PwYNGpSXB+jGjRsMGDAAPz8//Pz82LdvH++++y6zZ8/Oa/edd95hzpw5RfqMFIVjYWpBZ6/ObL+2nczswtN7ZGZnEpUcpSZ2KzJSSqN7tGzZUt7P6dOnHzhWlly6dEkCcs+ePVJKKUePHi1nzpwpb968mVfmueeek+vWrZNSSjly5Ei5YsUKKaUstEznzp3lhAkTpJRS/vXXXzIwMFBKKeV3330nBw4cKDMyMvLqp6eny7Zt28ro6GgppZTLli2To0ePllJK2bRpU7ljxw4ppZQTJ06UjRs3LvR9JCcny5SUFCmllOfPn5e51/rvv/+Wbdu2lcnJyfdobt26tfzjjz+klFKmpKTI5ORkuX37dtmnT5+8NseOHSsXLFggpZTSx8dHfvrpp3mvxcbG5v39zjvvyDlz5kgppRw8eLCcNWuWlFLKzMxMGR8fLy9duiRbtGghpZQyKytL1qpV6576uRj6u1Ce2Xx5s2yysIk8EHmg0DIRiRGyycImcuW5lWWoTFFSgGCpo8eWz6EeA+Hl5UX79u0BeO6555gzZw6+vr589tln3Llzh7i4OBo3bky/fvcuktm+fXuhZQYOHAhAy5YtuXz5MgBbtmzhlVdewcxM+3icnZ0JCQkhJCSE7t21iIysrCzc3d1JSEggPj6ezp07AzB8+HA2bNhQ6HvIyMgoMN9/Qbn4ExMTiYiIYMCAAQBYWVnpdJ2GDBmS93dhufy3bdvGL7/8AoCpqSmOjo44OjpStWpVjh49yo0bN2jRogVVq1bV6ZwK3Wjn0Q4rUyu2XNnCY+4Fr+pWET0VH2X8ReD+XPVCCF577TWCg4Px8vJi+vTpD+SqT01NfWiZ3Jz+pqamedk1ZQF58aWUNG7cmP37999zPD4+vsAc+oWRP99/dnZ2npkXds6CMDMzIzs7+573mJ/8GUVHjRrFmjVr8PPzY+HChezYseOh+l588UUWLlxIVFQUzz9fvC0DFYVjY25Dhxod2Hp1K28/9jYm4sHR3lzjVxuwVFzUGH8RuHr1ap7x/vbbb3mplF1cXEhKSiowiifXFB9W5n569OjBvHnz8n4I4uLiqF+/PjExMXnnz8jI4NSpUzg5OeHo6MiePXsAHpkvv7B8/wXl4ndwcMDT05M1a9YAkJaWxp07d/Dx8eH06dOkpaWRkJDA1q2Fb+hdWC7/wMBA5s6dC2h3L7dvawnEBgwYwD///MOhQ4fUTl+lRKBPIDEpMZyIOVHg6xFJEQhEsbZrVJQPlPEXgYYNG7Jo0SKaNWtGXFwcr776Ki+99BJNmzblySefpFWrVg/UcXJyemSZ+3nxxRfx9vamWbNm+Pn5sXTpUiwsLFi5ciWTJ0/Gz8+P5s2b503MLliwgLFjx9K2bVusra0f2nZh+f4Ly8W/ePFi5syZQ7NmzWjXrh1RUVF4eXkxePBgmjVrxrBhw2jRokWh5yssl//s2bPZvn07TZs2pWXLlpw6dQoACwsLunbtyuDBg1VEUCnR2bMzZiZmbL1a8A92RGIEbrZumJual7EyRVnxyHz8hkDl46+8ZGdn4+/vz4oVK6hbt26BZdR3oeS8uuVVLiVcYsPADQ8M8Y3cMBKARY8vMoQ0RTHRdz5+haJMOH36NHXq1CEwMLBQ01fohyDvICKSIjh369wDr0UkReBpr8b3KzJqcreCsnHjRiZPnnzPMV9fX1avXm0gRY+mUaNGhIWFGVpGpaCrd1dmHJjB5iubaeB8dwguIyuD6DvRKoa/gqOMv4LSs2dPNTmqKBRnK2daurVk65WtvN7i9bzj15OvI5EqlLOCo4Z6FIpKSpB3EBcTLhKWcPcuS6Vjrhwo41coKindvLsBsPXK3egeZfyVA2X8CkUlpbptdZq5NGPL1S15xyKTIjETZlSzqWZAZYrSRhm/QlGJCfIJ4vTN03k9/fCkcNxs3TAzUdN/FRll/Dqirzz3wcHBjB8/Xg+KSh87OztA5fivyAR5BwF3h3sikyJVqoZKQLn8Wf/03085G3dWr202cG7A5NaTH12whAQEBBAQoNMai2KRmZmZl9xNoXgUXg5e1K9Sn61XtzKi8QgikiLoWKOjoWUpShnV4y8CBeXjLyxHfmG59vPnso+JiaF79+74+/vz8ssv4+PjQ2xsLJcvX6Zhw4a89NJLNG7cmB49epCSklKori5dujB16lQ6d+7M7NmzC9VUUG79pKQkAgMD8ff3p2nTpqxdu7aUr6LC2Aj0CeRo9FHCE8OJTYlVE7uVAV3zN5flo7zk4//ss88KzZFfWK79/Lnsx44dKz/++GMppZQbNmyQgIyJiZGXLl2Spqam8ujRo1JKKZ9++mm5ePHiQrV17txZvvrqq1JK+dC8/QXl1s/IyJAJCQlSSiljYmJk7dq1ZXZ2tpRSSltb27z3/rAc/2WNob8LFY0LcRdkk4VN5CcHP5FNFjaR60LXGVqSohig73z8QohewGzAFPhRSvnAHoVCiC7AV4A5ECul7Kxr3fLC/fn4P/744wJz5OdSUK79/OzZsydvJW2vXr2oUqVK3mu+vr40b978ofXzk5sD/9y5cwVqKiy3fkZGBlOnTmXXrl2YmJgQERHBjRs3qF5dZWasLNR2qk1Nh5r8ceEPAJWuoRLwSOMXQpgC3wLdgXDgkBBinZTydL4yTsB3QC8p5VUhRDVd65Yn7k9mZW9vX2CO/FwKyrWfH/mQBHm5dXPrP2yoB+7mwJeF5O3PTXt8P7/++isxMTEcPnwYc3Nzatas+UB+fUXFRghBoHcgP4X8BKgY/sqALmP8rYFQKWWYlDIdWAb0v6/Ms8AfUsqrAFLK6CLULTfcn4+/TZs2BebI15UOHTqwfPlyADZt2sStW7dKrLGwvP2F5dZPSEigWrVqmJubs337dq5cuVJiDYryR3cf7Q7RwsQCF2sXA6tRlDa6GH8N4Fq+5+E5x/JTD6gihNghhDgshBhRhLrlhvvz8b/++uuF5sjXhffff59Nmzbh7+/Phg0bcHd3x97evkQaH5a3v6Dc+sOGDSM4OJiAgAB+/fXXe3LmKyoPjao2wt3WHQ87jwJ35VJULB6Zj18I8TTQU0r5Ys7z4UBrKeXr+cp8AwQAgYA1sB/oA/g9qm6+NsYAYwC8vb1b3t/zrIg52NPS0jA1NcXMzIz9+/fz6quvcuzYMUPLMnoq4nfBGNh6dSupman0qdXH0FIUxaAo+fh1mdwNB7zyPfcEIgsoEyulTAaShRC70Exfl7oASCnnA/NB24hFF/HlnatXrzJ48GCys7OxsLDghx9+MLQkRSUm0DvQ0BIUZYQuxn8IqCuE8AUigGfQxvTzsxb4RghhBlgAjwGzgLM61K201K1bl6NHj+pcfuzYsezdu/eeY2+88QajR4/WtzSFQlGBeaTxSykzhRDjgI1oIZk/SylPCSFeyXl9npTyjBDiH+AEkI0WthkCUFDd4oqVUj4QWVOZ+Pbbbw0tweA8amhSoVA8mnKz5+6lS5ewt7enatWqldr8KzNSSm7evEliYiK+vr6GlqNQGBX6HuM3Cjw9PQkPDycmJsbQUhQGxMrKCk9PtcBIoSgJ5cb4zc3NVS9PoVAo9IAK2FUoFIpKhjJ+hUKhqGQo41coFIpKhlFG9QghYoDCksa4ALFlKKeoKH0lQ+krGUpfySjP+nyklK66NGKUxv8whBDBuoYsGQKlr2QofSVD6SsZlUWfGupRKBSKSoYyfoVCoahklEfjn29oAY9A6SsZSl/JUPpKRqXQV+7G+BUKhUJRMspjj1+hUCgUJUAZv0KhUFQyjMb4hRC9hBDnhBChQogpBbwuhBBzcl4/IYTw17WuEei7LIQ4KYQ4JoQIvr9uGelrIITYL4RIE0JMLEpdI9BnDNdvWM7nekIIsU8I4adrXSPQV+rXT0eN/XP0HRNCBAshOuha1wj0Gfw7mK9cKyFElhDiqaLWzUNKafAHWq7+i0AttI1cjgON7ivTG9gACKANcFDXuobUl/PaZcDFwNevGtAK+AiYWJS6htRnRNevHVAl5+/HjfD7V6C+srh+RdBox915xWbAWSO7hgXqM5bvYL5y24C/gaeKe/2MpcffGgiVUoZJKdOBZUD/+8r0B36RGgcAJyGEu451DamvLHikPilltJTyEJBR1LoG1lcW6KJvn5TyVs7TA2jbiOpU18D6ygpdNCbJHKcCbAGpa10D6ysLdL0GrwOrgOhi1M3DWIy/BnAt3/PwnGO6lNGlriH1gfYF2iSEOCy0TeX1TUmugbFcv4dhbNfvBbS7u+LULQ4l0Qelf/1AR41CiAFCiLPAX8DzRalrQH1gBN9BIUQNYAAwr6h178dY8vEXtKXW/b+2hZXRpW5JKYk+gPZSykghRDVgsxDirJRyVxnrK426ulLScxjN9RNCdEUz1tzxX6O6fgXog9K/fjprlFKuBlYLIToBHwJButYtISXRB8bxHfwKmCylzBL37kJY5OtnLD3+cMAr33NPIFLHMrrUNaQ+pJS5/0YDq9FuzcpaX2nU1ZUSncNYrp8QohnwI9BfSnmzKHUNqK8srp/OGvNp2gXUFkK4FLWuAfQZy3cwAFgmhLgMPAV8J4R4Use691JakxVFnNgwA8IAX+5OTjS+r0wf7p08/VfXugbWZwvY5/t7H9CrrPXlKzudeyd3jeL6PUSfUVw/wBsIBdoV970ZSF+pX78iaKzD3clTfyAi5/+LsVzDwvQZxXfwvvILuTu5W+Trp9cPv4RvvDdwHm12+p2cY68Ar+T8LYBvc14/CQQ8rK6x6EObaT+e8zhlQH3V0XoGt4H4nL8djOj6FajPiK7fj8At4FjOI9jIvn8F6iur66ejxsk5Go4B+4EORnYNC9RnLN/B+8ouJMf4i3P9VMoGhUKhqGQYyxi/QqFQKMoIZfwKhUJRyVDGr1AoFJUMZfwKhUJRyVDGr1AoFJUMZfwKhUJRyVDGr1AoFJWM/weQGpbugZ2LvAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", + "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", + "plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", + "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", + "# plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8103139013452915\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8417475512225244 recall_benign=0.7919621749408984 recall_malware=0.8915329275041505\n", - "------thres= 0.03\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7994616419919246\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.8031390134529148\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8404093359737183 recall_benign=0.7813211845102506 recall_malware=0.8994974874371859\n", - "------thres= 0.049999999999999996\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7520188425302826\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7457399103139013\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8526483130612215 recall_benign=0.7918871252204586 recall_malware=0.9134095009019844\n", - "------thres= 0.06999999999999999\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7239793629430238\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the 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trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.4728577837595334\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.4780269058295964\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.84138201711121 recall_benign=0.756872852233677 recall_malware=0.925891181988743\n", - "------thres= 0.24999999999999997\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.4329295648272768\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.437219730941704\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 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"text": [ - "0.3566621803499327\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.34798206278026905\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - 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DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2711978465679677\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] 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- "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.20134529147982064\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " 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"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1320098698968147\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1078959174517721\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.10582959641255606\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8448693538241844 recall_benign=0.8846539618856569 recall_malware=0.8050847457627118\n", - "------thres= 0.38999999999999996\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.08770749214894571\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "\n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - }, + "data": { + "image/png": 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/MLGB7i3iP6u5KiwvZGnSUr5O/JpzxecIcA7g7oi7GddpnOH76VO2wdKHID8VBvwdBs7Uyh4bkE7q+OHoD8zdM5fCikJC3UK5NexWRoeMxs3OzaDbam5phWmMWjqKf/X9F7d1vs3U4SjXoVUn+I3HMpm15AAxAW708HenR4Ab3Tq4Ymfd9O6VtNwSFm49zbc7Uigoq6RbBxfu6xfM6Gg/bK0M031zobKK7KJybK0scLS1wtbKotV/iVTqKlmbspaFCQs5kHUAFxsXno59mvGh4w372csKYPUs2Pe1dqHUxE/AK9Qgqz6Rd4LZW2azL3Mfffz68FjMY0R7R7fYfzspJf2/68/IoJH8q++/TB2Och1adYKXUjb7f6qS8kqW7jnD/C3JHM8owsvJlslxAUzuE4CP89UvFiqrqOJMXilpuaWcyS0lLbdEe5ynPc4ovEDtXW5lIXC0tcLJ1goHG8uax462tR9X31e/7+ZgQ+9gD1ztW16xqH0Z+3hnzzvsSt/FsMBhvNj3RVxtXQ27kYTlsOKvUHlBm4qw132NHk5ZXlXOJwc/4dODn+Jk7cQzNzzDmJAxLTax1zZtzTSKK4r5dvS3pg5FuQ6tOsEbk5SSTUlZfPHnKdYfzcTaUjAmqj23xfpTVllFWnUC1xK5dssqulBnHVYWAj83Ozq6OdDR3Z6O7g74uNhSXqmj6EIlxdW3ogtV2uPyi69Vae9XP6+oqvvvZG0pGBDmzc3d/RgW7lvn/IK5q9JVseDwAt7d8y6e9p68duNr9PbrbdiNFJyD5Y/AyfXQeSSMfRecrm9Uz570PczeOptT+ae4JeQWnr3hWYOUfzYX/7fr//gm8Ru2Td5mtFIdStOpBN8MTmYWsWBLMot2p1FSXlXzurWloIObPR3c7S8lcQ97OlQ/9nWxw9Ki6Ud7FyqrKK7+EjiXX8avCedZdeg8Z/JKsbYU9A/14uZufgyP9MXN4eoXupiLhOwEZm2cxemC09zX7T5mxMwwbAlbnQ52zIPf/qWNrhn3HnQZdc3FCssL+d/u/7Ho2CI6OHXghT4v0L9Df8PFZSZ+OfkLMzfNZPGYxc13RbJicCrBN6OCsgq2nsjGy8mGDm4O+DjbYmGABN4YUkr2p+Wz6uA5Vh48R1puKVYWgn6hXtzcrR3DI9vh4Wjeyb6kooQ5u+aw6Ngiwj3CeTP+TYJdgw27kYxEWPIgpB/UumtG/PuKZYnXnl7La9tfI7ssm7vD7+axmMdabb2W5Pxkxiwfw8v9XmZC2ARTh6PoSSX4NkhKyaEzBaw8eI5fDp4jJacESwtB3xBPbu7ux4hIXzydzHe887qUdczeMpuyyjKe7f0sk8ImGbafu/KCNnH4n3PBIwTGf1hnOGV6cTqvbX+N31N/p6tHV2b3nd3qhw/qpI6+3/RlXOg4/hn3T1OHo+hJJfg2TkpJwtkCfqlO9snZJVgI6FOT7Nvh7Wx+yT6jJIPnNz/P1nNbGew/mJf6vYS7nbthN3Jqk9Y3n58GcQ+jG/wci5JX8faet6nQVfBozKNMiZjSZvqk7111LzqpY+HNC00diqInleCVGlJKEs8VsuqQ1o1zMrMYCwFDw32Z0jeQ/p28TNbF1BCd1PHV4a94e8/buNm68eqNr9KvfT/DbuRCIax7mRN7v+AlXz/2WkOcXxwv9nkRfxd/w27LzL25402WJC1h611bzW/aR6VB15PgrZo7GMW0hBBEtHchor0LTw3rzLH0IpbvO8MPO1P59XA6wV6OTI4L4LZe/mYxEsdCWHBP5D3E+cUxc+NMHv7tYe6JuIcnez55zSqJ+iq3suXT9sF8ktURx6oqXsnMZpyLNcKq7U0XGO4ZTmliKckFyXRy62TqcBQDU0fwbdSFyipWHzrPwq2n2XU6FztrC8ZGt2dKnyC6dzTwuPRGKqss47+7/8u3R76ls3tn3hzwJqHu13fhUmF5ISkFKaQUpnC64DQpBSnszdhLWlEao4JHMbPHX/Hc8SlsfhscPOGWORAxrnk+kBlKyk1i4k8Tee3G1xjTaYypw1H0oLpolOty+GwBX20/zfK9ZygpryLa340pfQIZHeVnkCuEm2pj2kZe+PMFiiuK+Xvs37mzy511TsAWlheSUphCSoGWxFMLU2vuc8py6qzLx8GHYJdg7o28lwEdB1x649wB+PExOH8AwsfAzXPAufXN+FVfpa6Svt/05bYut/HsDc+aOhxFDyrBK41SUFbBsj1nWLjtNMczinBzsOb2WH8mxwUQ6Gna7ous0ixe+PMFNp/ZTB+/Pvg4+Fw1iQe6BBLgHECASwCBzoH4u/jj7+x/9Snyqiph67uw/nWwtoMRr0HMZLOeVMQQJv8yGRsLG74Y+YWpQ1H0oBK80iRSSradzOGrbadZk3CeSp1kYGdvpvQJZHBXH4NcuNXYuL498i3v7n0XB2uHxiVxfWQdh58eh5QtEDJIqzfvHmSIj2CWXt32KitPruTPu/5svlm5FINRCV4xmPSCMr7bkco3O06TXnCBDm723NXbn7gQT7q2c8bZzvgnZo1RjwidDnZ/Dr+9CFJ3aYrAVjjSZGnSUl7c8iIrJ6wkwCXA1OEo16BG0SgG4+tix5NDw3h0cCfWJaazcNtp5vx6rOZ9fw97wtu5EO538eaMv7tDsw69NEqhLwsLuGEahI2AFX/TqlQeWgJj3wOfrs2/fSMK9wgH4HD2YZXgWxmV4BW9WFtaMLKbHyO7+XE+v4yEs/kcOV/I4XMFJJ4r4LfE9JoqmU62VnRp50y4n3NN4u/i64yjbQv8c3Pzh8mL4MAPWpL/6EaIf0arOW/ZAj9PA0LdQrG2sOZwzmFGBo80dTiKAbWOv1DFqNq52tHO1Y4h4ZcmkC4tr+JoeiGJ1Qn/yLlCftx7lq+2pQDaecpAD4eahN/J24kQb0eCvRzNYqTOVQkB0XdAp5tg9UzY8BqU5cHIBuedb3GsLa0Jcw8jMTvR1KEoBqYSvGIQ9jaWxPhrUyReJKUkLbe0Oulryf/wuQJWHTpf00YIaO9qT4i3Y03SD/HS7tu52JnVVbY4ecOkz8HBC7Z9AEEDoOvNpo7KIMI9wlmbstY45zcUo1EJXmk2Qgj8PRzw93BgeOSlMeUl5ZWczCzmZFYxJzOLOJVVzMnMYhbtSqW4Vilme2tLgrwcteTv5UjIxS8AbyecTNndM/wVSNmq1bSZvlnrxmnhIjwjWJK0hHPF52jv1N7U4SgGohK8YnQONlZ06+BKtw51r5iVUpJReIETmUXaF0BmMSezijhYXRJZV2vAl4+zLcHVyT/Yy5Hg6qN+f3cHbKwMN9RPSklBaSVpeSWczSvjTG4JWUXlDIj9P3r/OgGxZBpMXdni++MvnmhNzE5UCb4Vadl/lUqrIoTA18UOXxc7+nXyqvPehcoqTmeXcDKziBOZxSRnab8A1iSkk1NcXtPO0kLg725fJ+mHeDkSXN3lU7/7oUonSS8o42yeNq3imTxtqsWztR7X/lVx0XvAX+yn8Vrq25z78QV8x79mXt1J1ynMPQxLYUlCdgJDAoeYOhzFQFSCV1oEWytLOvs609nX+bL38krKOZVVXNPVc6o6+W89mU1Zha6m3cUuH393e/JKKziTW8r5gjKqdHWvBXFzsKaDmz2Bno706+RVM2NXezd7OrjZ42xnxcZjmfy4349FR/Zz6/4P+XuiN+163cy4mPZ0befS7PvD0Oys7AhxCyExR51obU3UhU5Kq6XTSdILy2r6+09lFnMqq4jU3FLcq5N4+1rJu2P18+sZzllUVIDu40HI4mxGlL3GeZ0bnX2dGBfTgbHR7fH3aDmzQT2/+Xk2ndnEhts3qBOtZkxdyaooxpSRCPMGU9E+lu/D5/Lj/vPsTM4FoEeAG+Oi23NLVHuznGSltq8Tv+aNHW+w7rZ1+Dhc3wTlivFcT4JXhScUpal8wuHmt7BO2cTd5UtYNL0fm2cOZtaorpRV6Jj982HiXlvLlM+2s2hXKgVlFaaOuEERnhEAajz8VVRW6cguuoBOZ5oD4+ul1xG8EGIk8A5gCXwqpXyj3vuuwFdAAFq//hwp5VVL06kjeKVVkRKWPqiVM7h3BQT1r3krKb2Qn/af5cd9Z0nJKcHGyoLxMe15KD6EUJ/LzymYSklFCX2+6cMj0Y/wSMwjpg6n2UkpKSmvIqe4XLuVlJNTVPdxdnE5uSXlNW3yS7UvZ29nW4ZH+DKqmx9xIR5YWxrvWNmgXTRCCEvgGDAMSAN2AndJKQ/XavNPwFVKOVMI4Q0cBdpJKcsbWieoBK+0QhcK4eN4qCiF6X+Co2edt6WU7EvNY/HuNJbsSaOsQsfQcB8eiu/EDUHuZtHvPXb5WAJdAnn3pndNHYpBXBx9dSKjqGb47YmsYjILysguLudCpa7B5awtBe4ONng4Xn5ztrNmz+lc1h/NoKS8Cld7a4aG+zKyWzsGhHk1+5XZhi421hs4LqU8Wb3y74BxwOFabSTgLLS/UCcgB6i8rqgVpaWzdYbb5sOnQ7WLoO76TitaVk0IQY8Ad3oEuPPUsM58ufU0X25N5vaPtxLj78b0gSEMi2hnsnLMoHXT7Drf8g68corLqxO4Noz2YkJPySmpc/1Ee1c7QrydCAv1wtPRBveLidvBBg+nS/fOtlZX/cJ94MZgyiqq2Hgsk9UJ5/nt8HmW7EnDwcaSwV18GNmtHYO7+pj2gjz0O4KfBIyUUk6rfj4FiJNSzqjVxhn4CegKOAN3SClXNrCuh4CHAAICAnqdPn3aUJ9DUczH9nmw6hkY/ir0e/yqTUvLq1i8O5VPNp0iJaeEIE8Hpg0IYVKvjiap0bMgYQFzds1hw+0b8LT3vPYCRnZxroKDZ/I4kVHMiUwtkeeWXDqvYWNlQYiXI518nOjk7USnWmUwHGyaJ+FWVOnYeiKb1Qnn+TXhPFlF5dhYWTAg1IsR3doxLNwXd0fDzCls6C6a24AR9RJ8bynl47XaTAL6A08BnYDfgGgpZcGV1qu6aJRWS0r4/m44thruXwMdr/1/sUonWX3oPPM2nmB/Wj6ejjbc2y+IKX0CDZYY9LHz/E7uX3M/Hw39iP4d+l97ASNKPFfAKysOs+VENgBeTjaEeNdK4j5OhHo70d7N3qS/gqp0kj0puaw6eJ41Cec5k1eKpYUgLtiDUd3aMTyyHb4udo1ev6ETfF9gtpRyRPXzfwBIKV+v1WYl8IaUclP189+BWVLKHVdar0rwSqtWmgsfxYMAHt4E9m56LXbxCHXexhOsP5qJvbUld9zgzwM3BhtlTH1BeQH9v+3Pkz2fZFr3ac2+PX1kFV3g/349xvc7U3Cxt+ZvQzszLqY9bg7G++JrLCklh84UsDrhHKsOnedkZjEAMwaH8vSILo1ap6H74HcCYUKIYOAMcCfwl3ptUoAhwCYhhC/QBTipf8iK0srYu2uVJ78YqU3/d/uXes3tKoSgbydP+nby5Fh6IfM2nuTr7Vpf/S1R7Xk4PuSyGj6G5GLjgr+zP4ezD1+7cTMrr9SxYEsyc9clUVpRxb39gnhySFiLSOwXCSHo3tGV7h1deWZEV5LSC1l96DwxAW5G2f41E7yUslIIMQNYgzZM8nMpZYIQYnr1+x8BrwDzhRAH0Y5ZZkops5oxbkUxf/43aFP9/fYv2Pkp9H7wuhbv7OvMnNuieXp4F7748xTfbE/h5/1n6dfJk3/eHN5siT7cI5yE7IRmWbc+pJT8djid135JJDm7hMFdvHnulghCfZxMFpOhhPk6E9ZAuY3moq5kVZTmpNPBN7fDqT9g2jrwi2r0qgrKKvh2ewqfbDpFfmk5fx3amYfjQ7Ay8Bjszw5+xtt73mbznZtxtW2+XwsNOXJe62f/83g2oT5OPH9LOIO6qKtqa1NXsiqKubCwgAkfgYMnLL5PGyvfSC521jw8sBO//S2e4ZHt+M+ao9z+8VaSs4oNGDCEe2qlg4/kHDHoeq8mu+gCzy07yM3vbCLhbAEvjY1k1ZMDVHJvIpXgFaW5OXrBrZ9CzklY8RQ08Vezu6MN793Vg3fujOF4RhGj3tnEV9tOY6hf47Vrwze38kodn2w8yaD/bOC7nanc0zeIDU8P4t5+QUa9OrS1UuWCFcUYgm6EgTNhw+sQMhB63N2k1QkhGBfTgd7BHjy7+ADPLz/E2sR03ro1Cp8mDMEDcLdzx8/Rj8M5zXeiVUrJ2sQM/r3yMMnZJQzq4s3zt4SbVemG1kB9RSqKscQ/o83j+sszkGGY7g8/V3sW3Nebl8ZGsu1kNsPf3sjKA+eavN5wj/BmO4I/er6QKZ/t4MEvd2FpIfjivhuYf19vldybgUrwimIsFpYw8ROwdoAf7oFyw/SdW1gI7u0XxMonBhDo4cBj3+zhr9/trSmM1RjhnuEkFyRTVF5kkBgByiqqeGPVEW6eu4mDZ/KZPSaC1X+NZ7DqZ282KsErijG5+Gn98VnHYMXfmtwfX1snbycWP9KPvw4N4+cD5xj59kb+PN640coXSwcfzT1qkNi2ncxm1Dub+OiPE9zaswMbnh7E1P7Bqp+9mam9qyjG1mkwDJoFB76HPQsMumprSwv+OrQzSx/ph72NJZM/3c7snxIoq7h8XtmrMVRt+IKyCv6x9CB3zttGlU7y9bQ43poUbdTyC22ZOsmqKKYQ/wykbINfnoX2PZs0Pr4h0f5urHx8AG+uPsL8LclsSsrkf3fEENXRTa/lvey98Lb3btIcrb8mnOeFHw+RWXiBBwcE87dhnZut2JfSMHUEryimcLE/3sFD648vyzf4JuxtLJk9NpKvHoijpLyKiR9s4Z21SVRUNVwDvb5wz/BGlSzIKCzj0a9389DC3bg72LDs0f48d0uESu4moBK8opiKkzdM+gLyUuDHGQbtj6/txjAvVj8Zz+goP/639hiTPtzC6kPnKbpw9Skbwj3COZl/ktLKUr22I6Vk0a5Uhv13I2sPZ/D08M78/PiNRPu7GeBTKI2hvlIVxZQC+8LQF7V6Nds/gj7NM1Weq4M1b9/Zg2ER7Xjhx0NM/2o31paCuGBPBnf14aauPgR7OdZZJtwzHJ3UcSz3GNHe0Vddf0p2Cf9cdpDNx7O4Icid1ydGtYraMS2dSvCKYmr9noDTW+HX56FDrFakrJncEuXH8EhfdiVrU879fiSDV1Yc5pUVhwn2cmRQF29u6upD72APIj0jAe1E65USfJVO8sWfp/i/X49haSF4ZXw3JvcOwMKE9diVS1SxMUUxB6W52nyuOh1M36T1zRtJak5JTbLfciKb8kodjjaW9OvkyQHLvzKw42DeGvTqZcslnitg1pID7E/LZ0hXH14Z3432bvZGi1tfVboqci/kkl2ard3Ksung1IEIzwjsrJp21a8pGHTCj+aiEryi1HNmD3w+AkIGwV3f15nP1VhKy6vYciKL349ksP5IBrku7yOsigm+8Dw3dfVhUBcfIvxc+GDDcT7ccAJXe2teHBvJmCg/o04arpM68i/kk1WaRXZZtnZfK4FffJ5VmkXuhVx08vITy1YWVkR4RBDjE6PdvGPwdvA22mdoLJXgFaWl2vEJ/PI0DHkRBjxl0lCklPxr01v8dOo7wkrfYW9KEVU6iZWFoFInmdijAy+Mjmj2Me1SSk4XnGZ3+m52p+9mT8Ye0ovTqZSXnyS2sbDB094TL3svPO08Lz2u9ZqbrRunC06zN3Mv+zP2cyjrEOW6cgA6OHWoSfYxPjGEuYVhaWH8uXGvxtAzOimKYiw3TIPTW+D3V8C/t1akzESEENwYEMPyU1/x0m2edLTvyx9JmexKzmFIuC8DOzfP0a5O6jied5zd6bvZdX4Xu9N3k12mzcPqYedBL99e3Bx8M572WgL3tLuUxJ2tnfX6JRHiFsLggMEAVFRVkJiTyN6MvezP3M/2c9tZeXIlAA5WDkR5R9Uk/SjvKJxtWk7NHHUEryjm5kIhzBuk3U/fDE6mq9WSWpDKzctu5sW+LzKp86Rm2UalrpKjOUfZlb6LXem72JO+h4LyAgDaObYj1jeWXr696OXbiyCXoGbvCpJScrb4LPsy9tUk/WO5x9BJHQJBqHsoj8c8XvMFYWzqCF5RWjJbZ20O109ugiUPwJTl2oVRJtDRuSPO1s4GrSxZXlVOQnZCzRH63oy9lFSWABDgHMDQwKE1Cb2DUweDbVdfQgg6OHWgg1MHbgm5BYDiimIOZh1kX8Y+1iSv4e9//J0Phn5AH78+Ro/veqgjeEUxV3u/gh8fg/hn4abnTBbGA2seoLSylG9u+abJ6zqZf5L7V99f0+US6hZKL99exPrG0tO3Jz4O5l9ZMv9CPlNXT+Vs0Vk+H/E5kV6RRt2+OoJXlNagx93a+PiN/4GAOAgdapIwwj3C+fbIt1ToKrC2sG70erJKs3h07aNIJP8d9F9ifWNxt3M3YKTG4WrrysfDPmbKL1N4ZO0jLBi1gGDXYFOH1SBVqkBRzNnN/wGfCFj6EOSfMUkI4Z7hlOvKOZV/qtHrKK0s5YnfnyC7NJv3h7zPsMBhLTK5X+Tj4MO84fMQQvDwbw9zvvi8qUNqkErwimLObBzg9gVQeUGbtLuq8ZN4NNbFSbgbU3gMtAuN/rHpHxzKOsSb8W/SzaubIcMzmUCXQD4c+iEF5QVM/206+RcMXzCuqVSCVxRz5xUGY+dC6nZYO9vomw90DsTeyr7RJ1rn7JrDupR1zOw9k5sCbjJwdKYV4RnB3MFzSSlM4dF1j1JSUWLqkOpQCV5RWoJut8IND8LW9+DISqNu2tLCUpujtRG14b9O/JqvEr/i7vC7mRw+uRmiM73efr15K/4tDmUd4qk/nqLCBL+yrkQleEVpKUb8G9r3gGWPQO5po2463DOcIzlHqNLpPzPU7ym/8+aON7nJ/yaejn26GaMzvaGBQ3mhzwv8eeZPnv/z+QZLI5iCSvCK0lJY2cJt86GiBHZ+atRNh3uEU1pZyulC/b5YDmUdYubGmUR6RvJG/Btmd7l/c5jUeRJP9nySX079wls738JUQ9Br0yvBCyFGCiGOCiGOCyFmNfD+M0KIfdW3Q0KIKiGE8crhKUpb4R6kFSM7vLzZJghpyMUTrfr0w6cVpvHYusfwtPfk3SHvYm9lfhUmm8sD3R5gSsQUvk78mnkH5pk6nGsneCGEJfA+MAqIAO4SQkTUbiOl/I+UMkZKGQP8A/hDSpnTDPEqihI5XpsF6tw+o20yxDUEW0vba46kyb+Qz2PrHqNCV8EHQz/Ay97LSBGaByEET8c+zZiQMby37z1+OPqDSePR5wi+N3BcSnlSSlkOfAeMu0r7u4BvDRGcoigN6HIzWFhBwnKjbdLKwoou7l2ueqK1vKqcv234GymFKbwz+B1CXEOMFp85sRAWvNT/JeI7xvPqtlf5NflX08WiR5sOQGqt52nVr11GCOEAjASWND00RVEa5OABwQNN0k2TmJ3Y4AlEKSUvbnmRned38kr/V7ihXfPNStUSWFtYM2fgHGJ8Ypi1aRbbzm0zSRz6JPiGSrdd6a9qDPDnlbpnhBAPCSF2CSF2ZWZm6hujoij1RY6H3GQ4f8Bomwz3CKeooogzhZdfUfv+vvdZcXIFM2JmMDpktNFiMmf2Vva8e9O7BLkG8eTvT5KQlWD0GPRJ8GmAf63nHYGzV2h7J1fpnpFSzpNSxkopY729zX/mFEUxW11uAWFp1G6amitac+r2wy9LWsbHBz5mQugEHop6yGjxtASutq58NPQj3O3ceWTtI00q99AY+iT4nUCYECJYCGGDlsR/qt9ICOEKDAR+NGyIiqJcxtETguON2k0T6haKlYVVnROtW89u5eWtL9PHrw8v9H3BqNP2tRQ+Dj58POxjk9StuWaCl1JWAjOANUAi8IOUMkEIMV0IMb1W0wnAr1LK4uYJVVGUOiLGQc5JSD9klM3ZWNoQ5hZWM1TyWO4xntrwFEGuQfx30H+bVGmytQt0CeSjoR/V1K3JK8szynb1GgcvpfxFStlZStlJSvnv6tc+klJ+VKvNfCnlnc0VqKIo9YSPMXo3TYRnBIk5iWSUZPDYusewt7Lnw6Eftqhp7Ewl3DOcd296l9TCVN7d+65RtqmuZFWUlsrRS5uz1YjdNOEe4eRdyOP+NfeTfyGf94e8TzvHdkbZdmtwQ7sb+HjYx/w99u9G2Z5K8IrSkkWMg+zjkNG4Ur7X6+KJ1tTCVOYMnFPzXNFfbLtYHKwdjLItleAVpSULHwPCAg4bZ2xDF48uhHuE82LfF4nvGG+UbSqNp6bsU5SWzMkHAvtr/fCD/9nsm7O1tOWHMaa9/F7RnzqCV5SWLmIcZB2FjMZNyKG0XirBK0pLFz4WEEbrplFaDpXgFaWlc/aFwH5GHS6ptAwqwStKaxAxHjITIfOoqSNRzIhK8IrSGoSPQXXTKPWpBK8orYGLHwT0Ud00Sh0qwStKaxExHjISICvJ1JEoZkIleEVpLcLHaPeHl5s0DMV8qASvKK2Fawfwj4ME1Q+vaFSCV5TWJGIcpB+E7BOmjkQxAyrBK0prEjFOu1fdNAoqwStK6+LaETreoIZLKoBK8IrS+kSMg3P7Ice4838q5kcleEVpbVQ3jVJNJXhFaW3cAqB9T9VNo6gEryitUuR4OLsXcpNNHYliQirBK0prVNNNo47i2zKV4BWlNXIPAr8YleDbOJXgFaW1ihwPZ3ZDXoqpI1FMRCV4RWmtarppfjJtHIrJqASvKK2VRwi0i1LDJdswleAVpTWLHA9pOyE/zdSRKCagEryitGYR47V71U3TJumV4IUQI4UQR4UQx4UQs67QZpAQYp8QIkEI8Ydhw1QUpVE8O4Fvd9VN00ZdM8ELISyB94FRQARwlxAiol4bN+ADYKyUMhK4zfChKorSKBHjIHU75J8xdSSKkelzBN8bOC6lPCmlLAe+A8bVa/MXYKmUMgVASplh2DAVRWm0yPHafeLPJg1DMT59EnwHILXW87Tq12rrDLgLITYIIXYLIe5paEVCiIeEELuEELsyMzMbF7GiKNfHKwx8IlU3TRukT4IXDbwm6z23AnoBtwAjgBeEEJ0vW0jKeVLKWCllrLe393UHqyhKI0WMg5RtUHDO1JEoRqRPgk8D/Gs97wicbaDNaillsZQyC9gIRBsmREVRmixyPCBVN00bo0+C3wmECSGChRA2wJ1A/TFXPwIDhBBWQggHIA5INGyoiqI0mncX8O6qumnamGsmeCllJTADWIOWtH+QUiYIIaYLIaZXt0kEVgMHgB3Ap1LKQ80XtqIo1y1iPJzeAoXppo5EMRIhZf3udOOIjY2Vu3btMsm2FaVNykiED/rAzXOg94OmjkZpJCHEbillrD5trZo7GEVRzIR3V/DqrJUQ1jfBV5RCcRYUZ166L8uD7reBk0+zhqs0nUrwitJWCKF102yaA6e3QtWFWsk7s24Sv/i4vKjhdZ34HSYv1tapmC2V4BWlLYkcDxvfgi9G1n1dWIKjFzh6a/fuQdpjB8/q17wvvZf4M/z2AhxaAt0nmeJTKHpSCV5R2hLfSLjre6gsrZu47dzAQs/ag30fg4SlsHoWhA4Be/dmDVlpPFVNUlHami4jIXICBN2oDZ908NA/uQNYWMKYuVCSA7/9q/niVJpMJXhFUa6fX5R2JL/nS0j+09TRKFegEryiKI0zaBa4BcLPT0LlBVNHozRAJXhFURrHxhFG/xeyk2DTf00djdIAleAVRWm80KHamPjN/4XMo6aORqlHJXhFUZpmxOtg7QA//xV0OlNHo9SiEryiKE3j5A3DX4WULbD3S1NHo9SiEryiKE3X424IGqANm1TFzMyGSvCKojSdEDD6f1rtmtWzTB2NUk0leEVRDMMrDOKf0a5yPfarqaNRUAleURRD6v9XrWrlyr/DhSsUKlOMRiV4RVEMx8oGRr8N+Smw4XVTR9PmqQSvKIphBfaFXvfBtg/g7D5TR9OmmVU1yYqKCtLS0igrKzN1KIqJ2dnZ0bFjR6ytrU0ditIYQ2fD0V/g5ydg2u9gaVapps0wq72elpaGs7MzQUFBCDWRQJslpSQ7O5u0tDSCg4NNHY7SGPZuMOpNWDQVtn8E/WaYOqI2yay6aMrKyvD09FTJvY0TQuDp6al+ybV0EeMhbASs/zfknjZ1NG2SWSV4QCV3BVB/B62CEHDLHEDAL0+DlKaOqM0xuwSvKEor4hYANz0PSb9CwjJTR9PmqASvKErzinsY/GJg1UwozTV1NG2KSvD1JCcn061bN73bT506lcWLFzdjRA273jgVxWQsLGHsXCjJht9eNHU0bYpK8EqTVVVVmToExdz5RUOfR2DPAji9xdTRtBlmNUyytpd+TuDw2QKDrjOivQsvjom8ZrvKykruvfde9u7dS+fOnfnyyy+ZM2cOP//8M6WlpfTr14+PP/74shOBL7/8coNtBg0aRFxcHOvXrycvL4/PPvuMAQMGUFVVxcyZM1mzZg1CCB588EEef/xxdu/ezVNPPUVRURFeXl7Mnz8fPz8/du/ezf3334+DgwM33njjVT9DcnIyU6ZMobi4GID33nuPfv36AfDWW2+xcOFCLCwsGDVqFG+88QbHjx9n+vTpZGZmYmlpyaJFi0hNTWXOnDmsWLECgBkzZhAbG8vUqVMJCgri/vvv59dff2XGjBkUFhYyb948ysvLCQ0NZeHChTg4OJCens706dM5efIkAB9++CGrVq3Cy8uLJ598EoDnnnsOX19fnnjiiev7B1ValsH/hMM/aVP8Td8MVramjqjV0+sIXggxUghxVAhxXAhxWak4IcQgIUS+EGJf9a1FT7V+9OhRHnroIQ4cOICLiwsffPABM2bMYOfOnRw6dIjS0tKapFfb1dpUVlayY8cO3n77bV566SUA5s2bx6lTp9i7dy8HDhxg8uTJVFRU8Pjjj7N48eKahP7cc88BcN999zF37ly2bt16zc/g4+PDb7/9xp49e/j+++9rkueqVatYvnw527dvZ//+/Tz77LMATJ48mccee4z9+/ezZcsW/Pz8rrkNOzs7Nm/ezJ133snEiRPZuXMn+/fvJzw8nM8++wyAJ554goEDB7J//3727NlDZGQkDzzwAAsWLABAp9Px3XffMXny5GtuT2nhLk7xl3UMPhsOR1aqCUKa2TWP4IUQlsD7wDAgDdgphPhJSnm4XtNNUsrRhgpMnyPt5uLv70///v0BuPvuu5k7dy7BwcG89dZblJSUkJOTQ2RkJGPGjKmz3Pr166/YZuLEiQD06tWL5ORkANauXcv06dOxstL+GTw8PDh06BCHDh1i2LBhgNb94efnR35+Pnl5eQwcOBCAKVOmsGrVqit+hoqKCmbMmMG+ffuwtLTk2LFjNdu87777cHBwqNlmYWEhZ86cYcKECYCWuPVxxx131Dw+dOgQzz//PHl5eRQVFTFixAgAfv/9d778UpsEwtLSEldXV1xdXfH09GTv3r2kp6fTo0cPPD099dqm0sKFDYMJ82DDa/DdX8C3Gwz4O0SM0/rqFYPSp4umN3BcSnkSQAjxHTAOqJ/gW436XS9CCB599FF27dqFv78/s2fPvuwinLKysqu2sbXVfo5aWlpSWVkJaFds1t+WlJLIyMjLjtLz8vKua2z4//73P3x9fdm/fz86na4maV9pmw2xsrJCV+sIq/5ndnR0rHk8depUli9fTnR0NPPnz2fDhg1XjW/atGnMnz+f8+fPc//99+v9uZRWIPoO6HYrHFoCm+bA4vvAq7OW6LtNUmUNDEifLpoOQGqt52nVr9XXVwixXwixSgjR4OG3EOIhIcQuIcSuzMzMRoRrHCkpKTUJ9ttvv63p7/by8qKoqKjBUTMXk9/V2tQ3fPhwPvroo5qEn5OTQ5cuXcjMzKzZfkVFBQkJCbi5ueHq6srmzZsB+Prrr6+67vz8fPz8/LCwsGDhwoU1J0KHDx/O559/TklJSc02XVxc6NixI8uXLwfgwoULlJSUEBgYyOHDh7lw4QL5+fmsW7fuitsrLCzEz8+PioqKOrENGTKEDz/8ENB+jRQUaOdVJkyYwOrVq9m5c2fN0b7ShlhaaYn+0W1w23ywtIFlD8N7vWD3AqgsN3WErYI+Cb6hw8b6h3x7gEApZTTwLrC8oRVJKedJKWOllLHe3t7XFagxhYeHs2DBAqKiosjJyeGRRx7hwQcfpHv37owfP54bbrjhsmXc3Nyu2aa+adOmERAQQFRUFNHR0XzzzTfY2NiwePFiZs6cSXR0NDExMWzZoo06+OKLL3jsscfo27cv9vb2V133o48+yoIFC+jTpw/Hjh2rOdoeOXIkY8eOJTY2lpiYGObMmQPAwoULmTt3LlFRUfTr14/z58/j7+/P7bffTlRUFJMnT6ZHjx5X3N4rr7xCXFwcw4YNo2vXrjWvv/POO6xfv57u3bvTq1cvEhISALCxsWHw4MHcfvvtWFqqn+ZtloUlRE7QTrre9R3Ye2gFyub2gO3ztBmilEYTV/p5XtNAiL7AbCnliOrn/wCQUl6x2LMQIhmIlVJmXalNbGys3LVrV53XEhMTCQ8P1zt4peXS6XT07NmTRYsWERYW1mAb9ffQBkkJJ36Hjf+BlK3g5Av9HofY+7WTtApCiN1Sylh92upzBL8TCBNCBAshbIA7gZ/qbbCdqO7YFUL0rl5v9vWFrbQVhw8fJjQ0lCFDhlwxuSttlBAQOgTuWwVTV2qzQ/36PPyvG2ycA2WGHTrd2l3zbIaUslIIMQNYA1gCn0spE4QQ06vf/wiYBDwihKgESoE75bV+GigGsWbNGmbOnFnnteDgYJYtM9+6HxERETXj4hWlQUJA0I3aLXWHltx/fwW2zIW46dB3Bti5mDpKs3fNLprmorpolGtRfw9KHWf3aaNuEn+GdlEwZRk4epk6KqMzdBeNoiiK6bWPgTu+gr8s0i6W+uJmKDhn6qjMmkrwiqK0LJ2Hw+TFUHAGvhipJhO5CpXgFUVpeYIHwD0/QmkefD4SMo+ZOiKzpBK8oigtU8dYbaSNrgK+GAXnD5o6IrOjErwBbdiwgdGjDVaOx2z89NNPvPHGG1d8f9euXaoSpGIa7brBfau1ypTzb4HUnaaOyKyYb9GHVbMM/43crjuMunKiMjdVVVXNcpXn9a537NixjB079orvx8bGEhur10l9RTE8r1Bt3PyX47TbX76D4HhTR2UW1BF8PcnJyXTt2pVp06bRrVs3Jk+ezNq1a+nfvz9hYWHs2LGDHTt20K9fP3r06EG/fv04evToZevp3r07eXl5SCnx9PSsqag4ZcoU1q5dS3JyMgMGDKBnz5707NmzphzBhg0bGDx4MH/5y1/o3r07VVVVPPPMM9xwww1ERUXx8ccfXzH2DRs2EB8fz4QJE4iIiGD69Ok1xcKcnJz417/+RVxcHFu3buWrr76id+/exMTE8PDDD9fUqlm9ejU9e/YkOjqaIUOGADB//nxmzJgBwKJFi+jWrRvR0dHEx8fXbPfiL5ecnBzGjx9PVFQUffr04cCBAwDMnj2b+++/n0GDBhESEsLcuXOb/G+lKDXcA+H+1docsF/fBsfWmDoi8yClNMmtV69esr7Dhw9f9pqxnTp1SlpaWsoDBw7Iqqoq2bNnT3nfffdJnU4nly9fLseNGyfz8/NlRUWFlFLK3377TU6cOFFKKeX69evlLbfcIqWU8uGHH5YrVqyQBw8elLGxsXLatGlSSilDQ0NlYWGhLC4ulqWlpVJKKY8dOyYv7o/169dLBwcHefLkSSmllB9//LF85ZVXpJRSlpWVyV69etW8V9/69eulra2tPHHihKysrJRDhw6VixYtklJKCcjvv/9eSqnt59GjR8vy8nIppZSPPPKIXLBggczIyJAdO3asWX92draUUsovvvhCPvbYY1JKKbt16ybT0tKklFLm5uZe9rlnzJghZ8+eLaWUct26dTI6OlpKKeWLL74o+/btK8vKymRmZqb08PCo2f6VmMPfg9LCFGVJ+VG8lC95SHloqamjaRbALqlnnjXfLhoTCg4Opnv37gBERkYyZMgQhBB0796d5ORk8vPzuffee0lKSkIIQUVFxWXrGDBgABs3biQwMJBHHnmEefPmcebMGTw8PHByciI/P7/Beu0AvXv3Jjg4GIBff/2VAwcO1FSnzM/PJykpqeb9+nr37k1ISAgAd911F5s3b2bSpElYWlpy6623ArBu3Tp2795dUxCttLQUHx8ftm3bRnx8fM26PTw8Llt///79mTp1KrfffntNjfvaNm/ezJIlSwC46aabyM7OJj8/H4BbbrkFW1tbbG1t8fHxIT09nY4dO17rn0NR9OfoCff+BN/cAYvvh/IS6NF2J5NRCb4BF2u3A1hYWNQ8t7CwoLKykhdeeIHBgwezbNkykpOTGTRo0GXriI+P5/333yclJYV///vfLFu2jMWLFzNgwADgyvXaoW6ddSkl7777rt4ldRuqZQ/aJB4X+92llNx77728/nrdenE//fTTNWvOf/TRR2zfvp2VK1cSExPDvn376rwvG7gy+uI6a+/X2nXxFcWg7Fzh7iXw3WT48VEoL4a4h0wdlUmoPvhGyM/Pp0MHrST+/PnzG2zj7+9PVlYWSUlJhISEcOONNzJnzpyaBH+leu31jRgxgg8//LDmV8KxY8dq5lltyI4dOzh16hQ6nY7vv/++wblbhwwZwuLFi8nIyAC0fvPTp0/Tt29f/vjjD06dOlXzen0nTpwgLi6Ol19+GS8vL1JTU+u8Hx8fX1MPfsOGDXh5eeHiomqGKEZm4wh/+R66joZVz8Cm/zN1RCahEnwjPPvss/zjH/+gf//+V0zMAHFxcXTu3BnQumzOnDlTk3CvVK+9vmnTphEREUHPnj3p1q0bDz/88FWPfPv27cusWbPo1q0bwcHBNdPw1RYREcGrr77K8OHDiYqKYtiwYZw7dw5vb2/mzZvHxIkTiY6OrjMl30XPPPMM3bt3p1u3bsTHxxMdHV3n/dmzZ7Nr1y6ioqKYNWtWzdyrimJ0VrbaZCLdb4d1L8Pal7RyxG2IKjbWimzYsIE5c+Y0OCF4S6T+HhSD0Olg5d9g93zo/TCMfAMsWu6x7fUUG1N98IqitG4WFjD6bbBxgq3vQXkRjJnbJuZ+bf2fsBU6ePAgU6ZMqfOara0t27dvb/CEr6K0eULA8FfB1hk2vA7H10KXmyF8NATFg5WNqSNsFirBt0Ddu3e/bPSKoijXIAQMmqVd0X7gezjwA+z+AmxdtQqVXUdD6FCwdTJ1pAajEryiKG1L11u0W0UpnNwAiSvg6C9wcBFY2UHIYO3IvvMobVx9C6YSvKIobZO1PXQZpd2qKrVJvo+s0BL+sVUgLCGwn3ZkHz4aXFveRXkqwSuKolhaaTXmgwdoo2zO7dMS/ZEVsHqmdvOL0RJ91zHg09XUEetFJXhFUZTahID2PbTbkBcg6zgc+VlL+L+/qt0C+sFNz2mTgpuxljsY1Ay11nrws2fPZs6cOQBMnTq1pi6OorQJXqFw49/gwXXwVCKMeA1yTmr15xeMhdQdpo7wisz2CP7NHW9yJOeIQdfZ1aMrM3vPNOg6m1NT6sE3Vy15RWnTXNpD38cg9n7Y+Rls/h98NgxCh2lH9O17mDrCOtQRfD0tvR68vsu+9dZbdO/enejoaGbNmgXAJ598wg033EB0dDS33norJSUlhty1itJ6WNtDvxnw5H4Y8iKc2QXzBmkFzs4fMnV0l+hbV9jQN1UPvnnqweuz7C+//CL79u0ri4uLpZSX6r5nZWXVrOu5556Tc+fOlVJqtdz/85//SCmlvPfee2tqzDc3c/h7UBS9lOZLuf4NKV/rKOWLLlL+cK+UGUeaZVOoevBN09LrwV9r2bVr13Lffffh4OAAXKr7fujQIZ5//nny8vIoKirSu0SxorR5di4waCb0flArh7DtIzj8I3S/DQbOBM9OJglLJfgGtOR68Posu3r16gbrvk+dOpXly5cTHR3N/Pnz2bBhg17bVBSlmoMHDPkX9HkU/nwbdnwKBxdDzF0Q/6w2taAR6dUHL4QYKYQ4KoQ4LoSYdZV2NwghqoQQkwwXovkx53rw+iw7fPhwPv/885o+9ot13wsLC/Hz86OioqKmpruiKI3g6KXVvnlyv3ZUf+AHeLcXrHgKCs4aLYxrJnghhCXwPjAKiADuEkJEXKHdm0Crn+3WnOvB67PsyJEjGTt2LLGxscTExNQMgXzllVeIi4tj2LBhdO3aMi7kUBSz5uwLo96EJ/ZCj7thzwJ4Jwa2vGeUzV+zHrwQoi8wW0o5ovr5PwCklK/Xa/dXoAK4AVghpbzqYGlVD165FvX3oLQ6ucnwx3+gy0gIH9OoVRi6HnwHoPa8bGlAXL0NdgAmADehJfgrBfYQ8BBAQECAPvEpiqK0Hu5BMP59o21OnwTf0CzM9Q/73wZmSimrrjZps5RyHjAPtCN4PWNU6rlaPXhFUZSL9EnwaYB/recdgfpnCWKB76qTuxdwsxCiUkq5/HoDklI2OMJDuaQt1IO/VtehoijXps8omp1AmBAiWAhhA9wJ/FS7gZQyWEoZJKUMAhYDjzYmudvZ2ZGdna3+c7dxUkqys7PrDB1VFOX6XfMIXkpZKYSYgTY6xhL4XEqZIISYXv3+R4YKpmPHjqSlpZGZmWmoVSotlJ2dHR07trz624piTq45iqa5NDSKRlEURbm66xlFo4qNKYqitFIqwSuKorRSKsEriqK0UibrgxdCZAKnr/C2F5BlxHCul7nHB+Yfo4qvaVR8TdOS4wuUUnrrsxKTJfirEULs0vckgimYe3xg/jGq+JpGxdc0bSU+1UWjKIrSSqkEryiK0kqZa4KfZ+oArsHc4wPzj1HF1zQqvqZpE/GZZR+8oiiK0nTmegSvKIqiNJFK8IqiKK2U0RP8teZ3FZq51e8fEEL01HdZM4gvWQhxUAixTwjRLIV29IivqxBiqxDighDi6etZ1gziM4f9N7n63/WAEGKLECJa32XNID5z2H/jqmPbJ4TYJYS4Ud9lzSA+k++/Wu0um9+6UftPSmm0G1o1yhNACGAD7Aci6rW5GViFNtFIH2C7vsuaMr7q95IBLxPvPx+0WbX+DTx9PcuaMj4z2n/9APfqx6PM8O+vwfjMaP85cencXhRwxMz2X4Pxmcv+q9Xud+AXYFJT9p+xj+B7A8ellCellOXAd8C4em3GAV9KzTbATQjhp+eypozPGK4Zn5QyQ0q5E21+3Ota1sTxGYM+8W2RUuZWP92GNsGNXsuaOD5j0Ce+IlmdkQBHLs3+Zi7770rxGYO+++BxYAmQ0Yhl6zB2gm9oftcOerbRZ1lTxgfaH8uvQojdQpt/1tCasg/MZf9djbntvwfQfq01ZtnGaEp8YCb7TwgxQQhxBFgJ3H89y5owPjCD/ScuzW9df56NRu0/fabsMyR95ne9Uht9lm2qpsQH0F9KeVYI4QP8JoQ4IqXcaOT4mmNZfTV1G2az/4QQg9ES6MU+WrPafw3EB2ay/6SUy4BlQoh44BVgqL7LNlFT4gPz2H9v0/D81o3af8Y+gtdnftcrtdFnWVPGh5Ty4n0GsAztZ5Wx42uOZfXVpG2Yy/4TQkQBnwLjpJTZ17OsCeMzm/1XK56NQCchhNf1LmuC+Mxl/12c3zoZmAR8IIQYr+eyl2uuEwpXOMlgBZwEgrl0oiCyXptbqHsSc4e+y5o4PkfAudbjLcBIY8dXq+1s6p5kNYv9d5X4zGL/AQHAcaBfYz+bieIzl/0XyqWTmD2BM9X/V8xl/10pPrPYf/Xaz+fSSdZG7T+DBX8dH/Jm4BjaGeHnql+bDkyvfiyA96vfPwjEXm1Zc4kP7ez2/upbggnja4f2bV8A5FU/djGj/ddgfGa0/z4FcoF91bddZvb312B8ZrT/ZlZvfx+wFbjRzPZfg/GZy/6r13Y+1Qm+sftPlSpQFEVppdSVrIqiKK2USvCKoiitlErwiqIorZRK8IqiKK2USvCKoiitlErwiqIorZRK8IqiKK3U/wO9cWUEWEJbSQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", + "plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", + "\n", + "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", + "\n", + "# plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", + "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.08475336322869956\n", - "Index(['Permission: FACTORY_TEST', 'Permission: DUMP',\n", - " 'Permission: BATTERY_STATS', 'Permission: BIND_WALLPAPER',\n", - " 'Permission: BIND_INPUT_METHOD', 'Permission: READ_LOGS',\n", - " 'Permission: INSTALL_LOCATION_PROVIDER',\n", - " 'Permission: READ_FRAME_BUFFER', 'Permission: SET_PROCESS_LIMIT',\n", - " 'Permission: SET_ANIMATION_SCALE',\n", - " ...\n", - " 'relative_humidity', 'rotation_vector', 'significant_motion',\n", - " 'stationary_detect', 'step_counter', 'step_detector', 'temperature',\n", - " 'if_the_app_using_suspicious_libs', 'proportion', 'label'],\n", - " dtype='object', length=355)\n", - "balanced acc=0.8397947888393749 recall_benign=0.8912297893189612 recall_malware=0.7883597883597884\n" + "------thres= 17\n", + "0.27706422018348625\n", + "0.2688679245283019\n", + "balanced acc=0.7474816072439163 recall_benign=0.810752688172043 recall_malware=0.6842105263157895\n", + "------thres= 18\n", + "0.2182175622542595\n", + "0.2112159329140461\n", + "balanced acc=0.7822428134506154 recall_benign=0.812624584717608 recall_malware=0.7518610421836228\n", + "------thres= 19\n", + "0.1817824377457405\n", + "0.17033542976939203\n", + "balanced acc=0.7950755624665922 recall_benign=0.8332280480101074 recall_malware=0.7569230769230769\n", + "------thres= 20\n", + "0.15176933158584535\n", + "0.139937106918239\n", + "balanced acc=0.7912549897637094 recall_benign=0.8671541742839732 recall_malware=0.7153558052434457\n", + "------thres= 21\n", + "0.13171690694626476\n", + "0.11740041928721175\n", + "balanced acc=0.8113282575500509 recall_benign=0.8592636579572447 recall_malware=0.7633928571428571\n", + "------thres= 22\n", + "0.11507208387942333\n", + "0.10377358490566038\n", + "balanced acc=0.8097022860180756 recall_benign=0.8719298245614036 recall_malware=0.7474747474747475\n", + "------thres= 23\n", + "0.0981651376146789\n", + "0.08962264150943396\n", + "balanced acc=0.8169408841620458 recall_benign=0.8911917098445595 recall_malware=0.7426900584795322\n", + "------thres= 24\n", + "0.08256880733944955\n", + "0.07651991614255765\n", + "balanced acc=0.821358045807224 recall_benign=0.8961407491486947 recall_malware=0.7465753424657534\n", + "------thres= 25\n", + "0.06828309305373526\n", + "0.0660377358490566\n", + "balanced acc=0.8448372615039281 recall_benign=0.9118967452300786 recall_malware=0.7777777777777778\n" ] } ], "source": [ "import collections\n", - "pd_metric=collections.defaultdict(list)\n", - "for thres in list(np.arange(0.01,0.4,0.02)):\n", + "# pd_metric=collections.defaultdict(list)\n", + "for thres in list(np.arange(17,26,1)):\n", " print('------thres=',thres)\n", - " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train,X_test,thres)\n", + " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train.drop('proportion',axis=1),\n", + " X_test.drop('proportion',axis=1),thres,['malwareNum','label'],2)\n", " pd_metric['thres'].append(thres)\n", " pd_metric['balanced_accuracy'].append(score)\n", " pd_metric['benign_recall'].append(recall1)\n", @@ -3939,324 +3003,100 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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thresbalanced_accuracybenign_recallmalware_recallbenign_precisionmalware_precision
00.010.8417480.7919620.8915330.6308850.948205
10.030.8404090.7813210.8994970.6558320.943761
20.050.8526480.7918870.9134100.7571670.927917
30.070.8479900.7776000.9183800.7876820.913825
40.090.8550420.7990870.9109980.7894740.915655
50.110.8590030.8052710.9127340.8029200.913916
60.130.8530250.7880060.9180440.8200290.901363
70.150.8491140.7745740.9236540.8406830.887361
80.170.8251430.7767650.8735210.7995310.857662
90.190.8430910.8198380.8663450.8299180.858054
100.210.8486590.8037560.8935620.8734690.832800
110.230.8413820.7568730.9258910.9177080.777165
120.250.8277840.7386450.9169230.9196430.731588
130.270.7935240.6540580.9329900.9481560.590057
140.290.7794580.6355830.9233330.9574860.482578
150.310.8166590.8293090.8040090.9437700.542857
160.330.8297560.8628080.7967030.9560570.531136
170.350.8313950.8634480.7993420.9646170.480237
180.370.8448690.8846540.8050850.9745860.452381
190.390.8397950.8912300.7883600.9784830.401617
\n", - "
" - ], "text/plain": [ - " thres balanced_accuracy benign_recall malware_recall benign_precision \\\n", - "0 0.01 0.841748 0.791962 0.891533 0.630885 \n", - "1 0.03 0.840409 0.781321 0.899497 0.655832 \n", - "2 0.05 0.852648 0.791887 0.913410 0.757167 \n", - "3 0.07 0.847990 0.777600 0.918380 0.787682 \n", - "4 0.09 0.855042 0.799087 0.910998 0.789474 \n", - "5 0.11 0.859003 0.805271 0.912734 0.802920 \n", - "6 0.13 0.853025 0.788006 0.918044 0.820029 \n", - "7 0.15 0.849114 0.774574 0.923654 0.840683 \n", - "8 0.17 0.825143 0.776765 0.873521 0.799531 \n", - "9 0.19 0.843091 0.819838 0.866345 0.829918 \n", - "10 0.21 0.848659 0.803756 0.893562 0.873469 \n", - "11 0.23 0.841382 0.756873 0.925891 0.917708 \n", - "12 0.25 0.827784 0.738645 0.916923 0.919643 \n", - "13 0.27 0.793524 0.654058 0.932990 0.948156 \n", - "14 0.29 0.779458 0.635583 0.923333 0.957486 \n", - "15 0.31 0.816659 0.829309 0.804009 0.943770 \n", - "16 0.33 0.829756 0.862808 0.796703 0.956057 \n", - "17 0.35 0.831395 0.863448 0.799342 0.964617 \n", - "18 0.37 0.844869 0.884654 0.805085 0.974586 \n", - "19 0.39 0.839795 0.891230 0.788360 0.978483 \n", - "\n", - " malware_precision \n", - "0 0.948205 \n", - "1 0.943761 \n", - "2 0.927917 \n", - "3 0.913825 \n", - "4 0.915655 \n", - "5 0.913916 \n", - "6 0.901363 \n", - "7 0.887361 \n", - "8 0.857662 \n", - "9 0.858054 \n", - "10 0.832800 \n", - "11 0.777165 \n", - "12 0.731588 \n", - "13 0.590057 \n", - "14 0.482578 \n", - "15 0.542857 \n", - "16 0.531136 \n", - "17 0.480237 \n", - "18 0.452381 \n", - "19 0.401617 " + "defaultdict(list,\n", + " {'thres': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],\n", + " 'balanced_accuracy': [0.8823969251039043,\n", + " 0.8680678629449121,\n", + " 0.8448637316561844,\n", + " 0.83506646951034,\n", + " 0.8257642844959918,\n", + " 0.8109771181199752,\n", + " 0.8041378182412909,\n", + " 0.8187374822286563,\n", + " 0.8302835749419288,\n", + " 0.7993311386032502,\n", + " 0.7707190505731385,\n", + " 0.7451151328605938],\n", + " 'benign_recall': [0.8568019093078759,\n", + " 0.831081081081081,\n", + " 0.7756813417190775,\n", + " 0.7833001988071571,\n", + " 0.7729831144465291,\n", + " 0.7568027210884354,\n", + " 0.7616892911010558,\n", + " 0.7804551539491299,\n", + " 0.7723970944309927,\n", + " 0.6973262032085561,\n", + " 0.6310772163965681,\n", + " 0.603035143769968],\n", + " 'malware_recall': [0.9079919408999328,\n", + " 0.9050546448087432,\n", + " 0.9140461215932913,\n", + " 0.8868327402135231,\n", + " 0.8785454545454545,\n", + " 0.8651515151515151,\n", + " 0.8465863453815261,\n", + " 0.8570198105081827,\n", + " 0.8881700554528651,\n", + " 0.9013360739979445,\n", + " 0.910360884749709,\n", + " 0.8871951219512195],\n", + " 'benign_precision': [0.7237903225806451,\n", + " 0.7263779527559056,\n", + " 0.7505070993914807,\n", + " 0.7124773960216998,\n", + " 0.7115716753022453,\n", + " 0.7142857142857143,\n", + " 0.7255747126436781,\n", + " 0.7783711615487316,\n", + " 0.8405797101449275,\n", + " 0.8716577540106952,\n", + " 0.8958051420838972,\n", + " 0.9107358262967431],\n", + " 'malware_precision': [0.9575070821529745,\n", + " 0.9464285714285714,\n", + " 0.9243816254416961,\n", + " 0.9195571955719557,\n", + " 0.90895410082769,\n", + " 0.888715953307393,\n", + " 0.8696369636963697,\n", + " 0.8584987057808455,\n", + " 0.8363794604003482,\n", + " 0.7560344827586207,\n", + " 0.6689478186484175,\n", + " 0.5393883225208527]})" ] }, - "execution_count": 51, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.DataFrame(pd_metric)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(pd_metric).to_csv('../../dnn_threshold.csv',index=False)" + "pd_metric" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "pd.DataFrame(pd_metric).to_csv('../../dnn_threshold_count.csv',index=False)\n", "df=pd.DataFrame(pd_metric)" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", @@ -4268,38 +3108,13 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 39, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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w5+tqi7o6u/LC0BeICYjhgXUPsDd3b7XlbGVj2kamfjuVRbsWMbrtaJZOXcoNcTdIcr8IpRSTYyazK2cXKQUp9g5HnEUSvLAPJ2ejHX7uBmjRFhbfDl/cajTlnMfHzYf/jvgvfu5+3PPTPaQVpdk8nMziTB5Y9wDzVs/DxcmFt0a9xTODnnH4vviOYnz0eJyUE0uTlto7FHEWSfDCvkI6wuxVMPxJOPC9UZtPXHJBsVCvUF4b/hrl5nLmrZ7HsqRlJOYmUmIqqdPuKy2VfJD4AZOWTOLn1J+596p7+WrSV/QN71un7TY3IV4hDGg1gGVJyzBbzJdeQTQIaYMXjiNzr9E2f2IHdJ5mnIT1Pnckxi0ZW7hvzX0UmYqsz7XybkV0QDTR/tHEBMQQ7R9NO/92l+zRsTN7J0/99hQHTh7g6oir+d++/0uUb1R9vLNm4YeUH3jw5wdZNHIR/Vv1t3c4TZacZBWNl9kEv7wI6/4FngHGCdhO5w4BYLKYOH7qOEkFSSTnJ5NUkERKQQopBSmUnzWJSrBnMDH+MbTzb2dN/NEB0bg6ufLi9hf56uBXhHiF8EifRxjReoR0t62jcnM5wz4fxpCoITwzqNrLZIQNyIQfovFydoXBD0KHsUZt/vNboOt1MPY58AoEwNXJ1aixB0TDWSMEmC1m0ovTSc5PJrnAGEc/pSCFZcnLKDYVW8s5KScUilvib+GeHvc0m8HB6pu7sztj2o1hefJyik3FclwdgNTgheMym2DD/xkXSHkFwYQXIW7cZW9Ga01WSZa1xp9RnMGEmAnEBcbZPuZmbkfWDmZ+P5N/DPgHU9tPtXc4TZI00YgLaK0pKDWRU1ROQWklYX7uhPt74uzUCJolTuwyavOZe6DbjTD2WfBsYe+oRDW01kxcMpEQzxDeHfOuvcNpkqSJppkwWzR5xRXkFJWTU1RObpFxP7uonJzCM8+ffq3Scu6XuauzIrKFF1GBXrQJ9KJ1oBetg6puA73wdneQj0d4N/jT2qrhDhYawxYHRoPFDNpS81LT604u0ONmGHgfuEkzgi0ppZgUM4mX/3iZ1MJUIn0j7R1SsyY1eAdXWmEmKbvIWLKKOJxdREpOCdmFZeQVV2Cp5s/n5uxEsI8bwb7uBPu4G/d93Amquu/n4UrmqTKO5pVwLLeEY3klHM0t5lRZ5TnbCfZxsyb71kHetA70ok2QF1EtvAj2ccPF2Q69bNN3wPrnwFQMyhmUk7E4nb6vqm7Pes36etVrhZlwaKUxecmIBUYbv5P0GLaVE0UnGP3VaOb1mMe87vPsHU6TI000Nqa15nBWEesPZrPhUA670woI8HQlzM+Dlv4exq2f+5n7/h6E+LhfVgLMK64gKbuIw1lnlqTsItLySzn9J3JS0CbIm+hgb0L9PAg5J4m7E1SVyP08XK6oR0hBiYmjecVVCb+E41W3x/JKOFFQes6XiVLQwsuNIG9jn8G+7gR5uxHia3yJBHmf+5yHq4NdCXr0N1j5KKT/ARG9YMyzENXH3lE1GXNWziG9OJ3vpn4nvZNsTBK8DeSXVPDL4Vx+PpjNz4eyOVFQBkB0iDcJbVpQXG4m41QZGQVlZBWWYTKfexydFAT7uBPmdzrpu9Oy6r6/pyvHT5YaSbyqVp5XXGFd18PViehgH2JDzywxIT60DfbC3cU+ibKi0kJafilHc4s5frKU7MJycs9rGsopqqCovLLa9X3cXaxfQC39PJjRtzUDY+18lajFArs+h9ULoCgDukw3avQB5/aFrzRbOFFQRurJUlJPlpB5qoyW/p60r/rbOExTlgNZmrSUxzY+xvtj3qdnWE97h9OkSIK/ApVmCztT81l/MIefD2azKzUfiwZfDxeujg1mcIcQBrUPJrLFhTMMWSyavJIKMgrKyDxVRsapMjJPlZNZcPq+cZtfYjpnvRZertbkHRvqQ0yoD7EhPkQEeOLUGE5+VqPMZD4v6RuJ/+znDmUVkV1YzuAOITw8piOdW9l3iNnK0lOUrPs/fLb+F61hS8QtLPW+juRTkHqylBMFZZirawurEhHgSWyoD+1DfWgf5kNsqC/tw3zw82i+MxyVmEoY+sVQxrUbx4IBC+wdjk2dLK7A2VnZ7e/bpBO8peofzRYJMPVkCT9XJfRfknIoLKvESUH3qAAGtw9hcIcQukf626ytucxkJrMq0Ue28CTIx90m221sykxmPvr9KK+sPUxBqYkpPSJ4YGQHogLrf3q+43klLNuVTkp2sVEjzy/hRH4ZlRZNK3J42PUzJjv/ShaBfO53O8nh44kM8iGyhSeRLbyIbOFJqK8HJwpKOVTVlHYos9B6v7zSYt1XSz+PqoTvQ/uqpN8+1IcAr+YxA9RjGx9jzbE1rL1+LR4utpmc3R601uzPKGT13kxW78tkZ2oBAL7uLoQHeBDu70mrAA9a+XsSHuBJK38PWgV40tLfo16aJpt0gl+x+wR3f7wdV2eFu4sz7i5OuLs44ebiZDx2Pe/x+a+7OFFWaebXw7kk5xgXv7Ty92BwByOhD4wJxt+r+da8GlJBqYnX1yfxzsYUtIaZ/dswf1gsLbxtmwArzRbWHsjmk01HWXcwG60hzM/dmrDPTt5RLbxoVbgTt9WPQ9o2aHWV0T7fut8l92O2aNJOlnKwKuEfyiq0nk8pqTgzPkuIrzsDYoIYGR/GkA4h+DbRmv7mE5uZ/eNs/jXoX4yLvvzrF+ypotLC5pQ8Vu/LZNXeTNLySwHoERXA8LhQ3F2dSM8vIz3f+IWXnl9K7lnNrKcFebsRXpX8WwV4El6V/Du38iM65MomjWnSCf5gZiErdp+gvNJCuclChdlMuclCeaWFikoL5ZXms+5X89hkRilFQtsW1lp6TIi3nAiyoxMFpby46hBfbjuOt5sLc4fGcMfAdni61a32k1FQxudbjvPZlmOcKCgj1NedG/u05sbeUbQKuMRk6xYL7P7SaJ8vTDfGxhn5d2NGqstksWjST9f4M4vYe+IU6w9mk1dcgauzol90EKPiwxgRH0a4f9OZBN6iLYz9aixt/dvyxsg37B3OJeWXVLDuQDar92Wy/kA2heWVuLs4Mah9MCM6hXFNXCihfjX/EikzmcmoSvbpBWWcyC8lvaCU9PwyTlTdnj5HNXdIDI+MvbIL7Zp0ghdN18HMQp774QCr92US5ufOX0Z0YHqvyMtqIrNYNBsO5/DJpqOs3peF2aIZ1D6Ym/u2YXinUFwvt7mtohh+fRk2vmj0oR9wL1z9F2NGqjowWzTbj51k1V6jhphS9WuyS4QfIzu1ZER8KPHhfo2+4vHyHy/z1u63+PHaHwnzrnmKRns5mlvMqqqmly1HTmK2aIJ93BnRKZQRncIYGBtc54rG2U6VmTiRX4a3u3O15/NqQxK8aNQ2p+Tx7Pf72H4sn9hQHx4a3ZGR8WEXTXY5ReV8uTWVTzcf41heCUHeblyXEMVNfaJoE2SDi5kK0uCnvxu9bnzC4KZPje6VNnI4q8iaaLYfO4nWxsnbEZ1CGRnfkr7RgZf/5eQAjp46yoRvJvDnnn9mdtfZDbZfrTXllRbKTGZKTWbKTBZKK8yUVZopLKvk9+RcVu/N5FCWMSppxzBfRsQbSb17ZIBDd3KQBC8aPa01KxMzeW7lfpKzi0lo04JHx8XRq03gOWV+T87j401HWZmYgcms6RcdyIy+bRjdOax+upSmbjUmJzFXwp3rLjpx+JXKLixn7f4sftybycbD2ZSZLPh6uDC0Yygj48MY2jGkUfXQmbliJqcqTrFk8pI6/SIpKDXx88FsNhzKJreooipxmyk1GU2vZx4bCf1iXJwUfdoFMqJTGCM6hdE6qP5P8NuKJHjRZFSaLXyxNZUXVh8ku7CcUfFhzBsaw/Zj+Xy86SjJ2cX4ebgwvVcUM/pGERvqW/9BZeyGt0ZCqx5w61Jwqb9eMaUVZjYezmH13kx+2p9JTpHRbn/PsFjuu6a9Q9c0T/vy4Jf847d/8On4T+kS3KXW62mtScouYs3+LH7al8XWo0YTSoCXKxEBnni6OuNhXZysjz3dnPFwccLDzRkPl6rHVa+7uzrj6epMp3A//D0bz5fk2STBiyanpKKSdzam8Pr6ZOuJqp6tA5jRtw0TuoU3/JWyuxfDV7Ohz50w7vkG2aXZotlxPJ/3fz3C0p3pDOkQwos39LB5ryNbO1VximGfD2Na+2k81u+xi5YtrzSzKTmPNfuzWLM/i2N5xoxdcS19Gd4plGviQukR1aJxDJJXTyTBiyYrt6icFXsy6NW6BfGt/OwbzMrH4LdXYMpr0GNGg+1Wa82nm4+zYGkiIb7u/PfmnnSPCmiw/V+JB9c/yG8nfmPNdWtwcz73CynrVBlrDxgJfcOhHEoqzLi7ODEwNphr4kIZFhdKxKV6PTUjkuCFaAjmSvhoKhzbBLNXGn3mG9Cu1HzmfbSd7MJynpwUz4w+rR22182G1A3c/dPdvDD0Ba6JGs6e9AJ+2pfF2gNZ7Kq6cKiVvwfD4kIZ3imU/tG27b3SlEiCF6KhFOfCoqFGF8q71oN3w46vc7K4gj9/voP1B7OZ1jOCp6d0dcjEWGmpZOTikbTxjiMp8TqO55WiFPRs3YJr4oyml7iWvg77BeVIZDx4IRqKdxDc8CG8Mxq+nAUzl4Bzw/1btfB2491ZvXl5zWFe/Okge9NP8dotvWgX7Fjj3Ls4udDZbyjrMr7Gl7H8+7qeDO0Y0myH62goja9jrRCOplUPmPgfOLIBVj3R4Lt3clLcP6I9793eh4xTZUx6eSMrEzMaPI6aaK15de1hVvwWiVIWbhuZx7W9IiW5NwBJ8ELYQvcboe9c+P1V2PWlXUIY0iGE5fdeTXSIN3d9uI1nvt9Hpfni/cHrW3mlmf/5YifPrzzAxE49iWvRiTWpK+waU3NSq9+SSqkxwH8AZ+AtrfWz573uD3wEtK7a5kKttUzIKJqXUf80+sgvvRdCOhpTDTawyBZefDG3P08t38sb65PZeTyfl266ilDfhh/NMaeonLs+3Ma2oyf5n5EdmH9NLJ/sn8yzm59l1dFV9A7rTYBHQIPHVVtaawrKC0gtSiW1KJW0wjTSitLIKM6gY2BHJsdMpq1/W3uHeVGXPMmqlHIGDgIjgVRgC3CT1nrvWWX+F/DXWj+slAoBDgAttdYXDq9WRU6yiiapKMs46erkDHeuB6/AS65SX77ensr/frMbPw9XXr25J73bNlwsBzIKmf3+FnKKyvn3dT0Y3y0cgJNlJ5nwzQROVZwCINAjkJiAGGL8Y4zbgBii/aMJ8gxqkDhLTCWkF6WTVpRmJPLCVNKK0qxLsan4nPIB7gGEeIWQlJ+ERVvoEdKDKbFTGN12ND5udRufqLZs2otGKdUfWKC1Hl31+FEArfUzZ5V5FIgC7gHaAquADlrrGn8fSoIXTVbqNnh3DLQZADd/1aAnXc+378Qp5n20jeMnS3l0bByzr25X7z1V1u7P4t5P/8DLzZm3bkugW2TAOa/nleWxN3cvSflJxlKQRHJ+MkWmImuZFu4tiA6IJsY/huiAaGIDYokJiCHII6ja+M0WM0WmImOpKKKwopDCikKKTEXn3J5eMkoySC1MJa8s75zteDh7EOkbSYRPhHU5/TjSNxJvV+PkdXZJNsuSl7Hk8BJSClLwcPZgRJsRTImdQu+WvXFS9df6besEPx0Yo7WeU/V4JtBXaz3/rDK+wFIgDvAFbtBaf1fNtu4E7gRo3bp1r6NHj9buHQnR2Gz/EJbOh4H3w8h/2DWUU2Um/vrFTn7cm8m4ri15bnp3fOphmkGtNe/8coSnv9tLp3A/3rotodbDH2utySrJsib8pPwkkguSOZx/mMKKQms5Pzc/2vm3w0k5nZPEz69pV8fd2R0fVx983XwJ8wojwrcqgftEWu/X9AVysbh35+xmyeEl/JDyA4WmQlp5t2JS7CQmx0wm0jey1tuqLVsn+OuA0ecl+D5a63vPKjMdGAg8AMRg1OC7a61P1bRdqcGLJm/5A7D1bZj+LnSZZtdQtNYs+jmZf/2wn7bB3vx5RAeGdbTdZCMms4Unvk3k083HGN05jBdu6IGXW92/RLTW5JTmnEn6+cmknErBCSd83IxkfTppn771dfM1XnP1tZbxdfXF1bl+x54pqyxjzbE1LDm8hN9P/I5GkxCWwJTYKYxsMxIvV9sMaGaPJprvgGe11huqHq8BHtFab65pu5LgRZNXWQHvTzBOvM5ZDWGd7R0RvyXl8sAXOzhRUIabsxMDY4MY3bklI+LDCL7Cbov5JRXc/fF2fk3K5e6hMfx1VMdGMQhafTpRdIJlycv49vC3HCs8hpeLF6PajmJK7BR6hvasUzOZrRO8C8ZJ1uFAGsZJ1hla68SzyrwGZGqtFyilwoDtGDX4nJq2KwleNAuFGfDGEHD1hDvXgmcLe0dknWxk5Z4MfkjMIPVkKU4KEtoGMrpzS0Z3Dqv1ZBTJ2UXMfn8raSdLeWZaV67tZfsmicZMa832rO18e/hbVh5ZSUllCVG+UdzV7S4mx06+om3afKgCpdQ44EWMbpLvaK2fVkrNrXoDryulWgHvAeGAwqjNf3SxbUqCF83G8c3w7jiIHgozPjd62DgIrTV7T5xiZWImPyZmsD/DaO/uEuHH6PiWjO7SkvahPtXWOH89nMO8j7fj7KR4Y2avBu2l0xiVmEpYdXQVSw4vYUzbMdwQd8MVbUfGohHC0Wx9B5b/BQb9FYb/zd7R1OhITjErEzNYmZjB9mP5AEQHezOqqmZ/erajTzYd44lv99Au2Jt3ZvUmKrDxTJjhCLTWV9xMIwleCEejNSy7D7Z/ADd8BJ0m2juiS8o8VcaPe42a/W9JuVRaNC39POjY0pf1B7MZ0iGEl2dc1ahml2oKJMEL4Ygqy42mmuz9MPtHhzjpWlsFJSZ+2p/JysQMNqfkMa1nJI+OjbusCdGFbUiCF8JRnUqHN68BSyXMWgEhHewdkWhkLifBy9evEA3JrxXctgxQ8P5EyE2yd0SiCZMEL0RDC24Pty0FiwnenwQn5YpuUT8kwQthD6GdjMlBKoqMmnxBqr0jEk2QJHgh7CW8G8z8BkpPGjX5QseZpEM0DZLghbCniJ5wy1dQlGkk+aJse0ckmhBJ8ELYW1QfmPEF5B+DDyZDSd6l1xGiFiTBC+EI2g6Emz6F3MNGki89ae+IRBMgCV4IRxEzDG782LgQ6qNroazG0baFqBVJ8EI4kvYj4br34cRO+Hg6lBddeh0haiAJXghHEzcOrn0bUrfCpzdCRYm9IxKNlCR4IRxR5ykw9Q04shE+mwGmMntHJBohSfBCOKpu18HkVyB5LXxxqzFDlBCXQRK8EI7sqltgwgtwaCUsvh3MJntHJBoRSfBCOLqEO2Dsc7B/OXz9JzBX2jsi0UjUfdpzIUT963uXMZ78qr+BsxtMec2hpv4TjkkSvBCNxcD7wFwOa/4JbQZCr9vsHZFwcNJEI0RjMuivEBgNe7+1dySiEZAEL0RjohTETYCUn6GswN7RCAcnCV6IxiZugjFZyKFV9o5EODhJ8EI0NpEJ4B0K+7+zdyTCwUmCF6KxcXKGjmONGnxlub2jEQ5MErwQjVHcBKgoNNrihaiBJHghGqN2g8HNx7j4SYgaSIIXojFy9YDYEbB/BVgs9o5GOKhaJXil1Bil1AGl1GGl1CPVvP6gUmpH1bJHKWVWSgXaPlwhhFXcBCjOgrSt9o5EOKhLJnillDPwKjAWiAduUkrFn11Ga/281rqH1roH8CiwXmstE0sKUZ/ajwQnF2mmETWqTQ2+D3BYa52sta4APgMmX6T8TcCntghOCHERngFGW/y+5aC1vaMRDqg2CT4COH7W49Sq5y6glPICxgBf1fD6nUqprUqprdnZ2ZcbqxDifHHjIS8Jcg7aOxLhgGqT4FU1z9VUXZgI/FJT84zWepHWOkFrnRASElLbGIUQNek4zriVZhpRjdok+FQg6qzHkUB6DWVvRJpnhGg4fq0gopdc1SqqVZsEvwVor5Rqp5Ryw0jiS88vpJTyB4YAMsydEA0pbjykbYNTNdW7RHN1yQSvta4E5gMrgX3AF1rrRKXUXKXU3LOKTgV+1FoX10+oQohqxU0wbqUWL86jtJ3OvickJOitW6X/rhB1pjW8kgD+UXDrEntHI+qZUmqb1jqhNmXlSlYhGjuljGaaIxugNN/e0QgHIgleiKYgbiJYKmWMeHEOSfBCNAURvcAnTLpLinNIgheiKXByMvrEH14NpjJ7RyMchCR4IZqKuAlQUSRjxAsrSfBCNBXtBoGbrzTTCCtJ8EI0FS7uxgiTB1aAxWzvaIQDkAQvRFMSNx6KsyF1i70jEQ5AErwQTUn7keDkKs00ApAEL0TT4uEP0UNkjHgBSIIXoumJGw8nUyB7v70jEXYmCV6IpkbGiBdVJMEL0dT4toTI3jK6pJAEL0STFDce0v+AglR7RyLsSBK8EE3R6THiD3xv3ziEXUmCF6IpCm4PwR1g3zJ7RyLsSBK8EE1V3Hg4shFKT9o7EmEnkuCFaKriJoI2w8Ef7R2JsBNJ8EI0Va2uAt9w6S7ZjEmCF6Kpso4R/xOYSu0djbADSfBCNGVx48FUDMnr7R2JsANJ8EI0ZW0HgbufNNM0U5LghWjKXNyg/SijP7yMEd/sSIIXoqmLGw8lOXB8s70jEQ1MErwQTV3sCHB2k2aaZkgSvBBNnYcfRA81EryMEd+sSIIXojmIGw8nj0DWXntHIhpQrRK8UmqMUuqAUuqwUuqRGsoMVUrtUEolKqWkT5YQjqTDWEDJEMLNzCUTvFLKGXgVGAvEAzcppeLPKxMA/BeYpLXuDFxn+1CFEFfMNwyi+kg7fDNTmxp8H+Cw1jpZa10BfAZMPq/MDOBrrfUxAK11lm3DFELUWdx4OLET8o/bOxLRQGqT4COAsz8RqVXPna0D0EIptU4ptU0pdWt1G1JK3amU2qqU2pqdnX1lEQshrox1jPgV9o1DNJjaJHhVzXPnn4p3AXoB44HRwN+UUh0uWEnrRVrrBK11QkhIyGUHK4Sog6AYCImTZppmpDYJPhWIOutxJJBeTZkftNbFWusc4Gegu21CFELYTNx4OPILlOTZOxLRAGqT4LcA7ZVS7ZRSbsCNwNLzynwLDFJKuSilvIC+wD7bhiqEqLO48cYY8dJM0yxcMsFrrSuB+cBKjKT9hdY6USk1Vyk1t6rMPuAHYBewGXhLa72n/sIWQlyRVj2Nqfy2vWfvSEQDcKlNIa31CmDFec+9ft7j54HnbReaEMLmlIKEO+CHR4weNeHSktqUyZWsQjQ33W8CF0/Y8ra9IxH1TBK8EM2NZwB0nQ67v4SyAntHI+qRJHghmqPec8BUAjs/s3ckoh5JgheiOWrVAyJ6Gc00MsJkkyUJXojmqvccyDkARzbaOxJRTyTBC9FcdZ4KHgGw5S17RyLqiSR4IZorV0+46hZj6ILCDHtHI+qBJHghmrOEO8BSCds/sHckoh5IgheiOQuKgZhrjCtbzZX2jkbYmCR4IZq7hNlwKg0O/mDvSISNSYIXornrMAb8ImCrXNna1EiCF6K5c3aBXrMgaQ3kJtk7GmFDkuCFENDzVnByga3v2DsSYUOS4IUQ4NvSmNLvj4/AVGrvaISNSIIXQhh6z4GyfEj8pv72UZwDbwyBDybDxhcg/Q+wmOtvf81crcaDF0I0A22vhuCOxpWtPWbYfvsWC3wzF7L2Gd0zVy8wnvdsAW0HQfRQYwmMNsatF3UmCV4IYVAKes+G7x8yatatrrLt9n9/FQ6vgvH/Nn4tFGZCys+Qsg6S1sG+qplA/aMgeghED4N2g8En1LZxNCNK22kkuYSEBL1161a77FsIUYOyAvh3HHS5Fia/Yrvtpm2Dt0dBx7Fw/YcX1tC1hrxkSF4LyeuNxF+Wb7wW2vlM7b7NAHD3sV1cjZBSapvWOqFWZSXBCyHOsfQ+2PUF/M9+Y3KQuiorgDcGG23tczcYTTKXYjEbUwomrzOWY7+Dudzo6RPZG0b8HVr3rXtsjdDlJHg5ySqEOFfv2VBZCjs/rfu2tIZlf4b843Dt27VL7gBOzhDREwY9ALcthUeOwq3fwoB7jW19ch1kH6h7fE2cQ7XBm0wmUlNTKSsrs3cows48PDyIjIzE1dXV3qE0P+HdjVrylreh79y6nfD840NI/BqGP1G3Grer55lmml6z4K0R8PF0mPOTtNFfhEMl+NTUVHx9fWnbti1KzqI3W1prcnNzSU1NpV27dvYOp3lKmA1L5hpt4dFDrmwbWfthxUNGUh74F9vF1qItzPgc3psAn9wAs5aDm7fttt+EOFQTTVlZGUFBQZLcmzmlFEFBQfJLzp46TzWaU650fBpTKSy+3TghOnURONk41UT0Mpp80v+Ar+ZIX/oaOFSCByS5C0A+B3bn6mFMBrJvOZw6cfnr//AoZO2Fqa+Db5jt4wOIGwdj/wUHVhj7k7llL+BwCV4I4SAS7gBtvvzJQBK/gW3vwsA/Q+yIegnNqu9d0O8e2PwG/P7f+t1XIyQJXghRvcBoiBl+eZOBnDwCS++HiAS45vH6jO6MUf+EThNh5WOwd2nD7LORkAR/niNHjtClS5dal581axaLFy+ux4iqd7lxCnFFes+BwnQ4+P2ly5pNsHi2cX/62+DcQD2gnJxg2psQmQBf/wmOb2mY/TYCtUrwSqkxSqkDSqnDSqlHqnl9qFKqQCm1o2p5wvahCkdlNssJriarw2jwizTGp7mUNf+EtK0w6T9GT5eG5OoJN30GvuHw6Q3GVbHi0t0klVLOwKvASCAV2KKUWqq13nte0Q1a6wm2CuzvyxLZm37KVpsDIL6VH09O7HzJcpWVldx222388ccfdOjQgQ8++ICFCxeybNkySktLGTBgAG+88cYFJwL/8Y9/VFtm6NCh9O3bl7Vr15Kfn8/bb7/NoEGDMJvNPPzww6xcuRKlFH/605+499572bZtGw888ABFRUUEBwfz3nvvER4ezrZt27jjjjvw8vLi6quvvuh7OHLkCDNnzqS4uBiAV155hQEDBgDw3HPP8eGHH+Lk5MTYsWN59tlnOXz4MHPnziU7OxtnZ2e+/PJLjh8/zsKFC1m+fDkA8+fPJyEhgVmzZtG2bVvuuOMOfvzxR+bPn09hYSGLFi2ioqKC2NhYPvzwQ7y8vMjMzGTu3LkkJxv/cK+99hrff/89wcHB3H///QA89thjhIWFcd99913eH1TUPydnSJhlJO+cwxAcW325w6vhlxeh1+1GDxx78A6GmxfD2yPgo+kwZzV4BdonFgdRmxp8H+Cw1jpZa10BfAZMrt+w7OvAgQPceeed7Nq1Cz8/P/773/8yf/58tmzZwp49eygtLbUmvbNdrExlZSWbN2/mxRdf5O9//zsAixYtIiUlhT/++INdu3Zx8803YzKZuPfee1m8eLE1oT/22GMA3H777bz00kv89ttvl3wPoaGhrFq1iu3bt/P5559bk+f333/PkiVL2LRpEzt37uShhx4C4Oabb+aee+5h586d/Prrr4SHh19yHx4eHmzcuJEbb7yRadOmsWXLFnbu3EmnTp14+22je919993HkCFD2LlzJ9u3b6dz587Mnj2b999/HwCLxcJnn33GzTfffMn9CTu56hKTgRRmwNd3QWg8jHmmYWM7X3As3PgpFKTCpzeBqXl3ta3NhU4RwPGzHqcC1V2S1l8ptRNIB/6qtU48v4BS6k7gToDWrVtfdKe1qWnXl6ioKAYOHAjALbfcwksvvUS7du147rnnKCkpIS8vj86dOzNx4sRz1lu7dm2NZaZNmwZAr169OHLkCACrV69m7ty5uLgYf4bAwED27NnDnj17GDlyJGA0f4SHh1NQUEB+fj5DhhgXncycOZPvv6+5XdRkMjF//nx27NiBs7MzBw8etO7z9ttvx8vLy7rPwsJC0tLSmDrVqHl5eHjU6jjdcMMN1vt79uzh8ccfJz8/n6KiIkaPHg3AmjVr+OADoxeGs7Mz/v7++Pv7ExQUxB9//EFmZiZXXXUVQUFBtdqnsAPfMOg0CXZ8ZJw4dfM685rFAl/fCRXFMP0do6nE3tr0h6mvweI7YMk8o7+8rfvhNxK1SfDVdUg+v8PpdqCN1rpIKTUOWAK0v2AlrRcBi8AYbOzyQm045ze9KKW4++672bp1K1FRUSxYsOCCi3DKysouWsbd3R0wklxlpdEjQWt9wb601nTu3PmCWnp+fv5l9Q1/4YUXCAsLY+fOnVgsFmvSrmmf1XFxccFisZzzHs/m7X3m6sFZs2axZMkSunfvznvvvce6desuGt+cOXN47733yMjI4I477qj1+xJ20nu2MeRA4tdG//jTfnkBUtbDxJcgtJP94jtfl2uNMWtWPwkBrWHk3+0dkV3U5mstFYg663EkRi3dSmt9SmtdVHV/BeCqlAq2WZQN7NixY9YE++mnn1rbu4ODgykqKqq218zp5HexMucbNWoUr7/+ujXh5+Xl0bFjR7Kzs637N5lMJCYmEhAQgL+/Pxs3bgTg448/vui2CwoKCA8Px8nJiQ8//NB6InTUqFG88847lJSUWPfp5+dHZGQkS5YsAaC8vJySkhLatGnD3r17KS8vp6CggJ9++qnG/RUWFhIeHo7JZDontuHDh/Paa68Bxq+RU6eM8ypTp07lhx9+YMuWLdbavnBgbQZCSNy5J1uPbYI1T0Pnacacro5m4P1GX/5fXmy2c83WJsFvAdorpdoppdyAG4FzOpsqpVqqqmqhUqpP1XZzbR1sQ+nUqRPvv/8+3bp1Iy8vj3nz5vGnP/2Jrl27MmXKFHr37n3BOgEBAZcsc745c+bQunVrunXrRvfu3fnkk09wc3Nj8eLFPPzww3Tv3p0ePXrw66+/AvDuu+9yzz330L9/fzw9L/5T+O677+b999+nX79+HDx40FrbHjNmDJMmTSIhIYEePXqwcOFCAD788ENeeuklunXrxoABA8jIyCAqKorrr7+ebt26cfPNN3PVVTVPAPHUU0/Rt29fRo4cSVxcnPX5//znP6xdu5auXbvSq1cvEhONljs3NzeGDRvG9ddfj7Oz8yWPlbAzpYzxadL/MMZ2Lz0JX82GgCiY+KJjzsCkFIx9HtqPgu/+Bw7+aO+IGlytxoOvanZ5EXAG3tFaP62UmgugtX5dKTUfmAdUAqXAA1rrXy+2zerGg9+3bx+dOjnQzzxRbywWCz179uTLL7+kffsLWvMA+Tw4nLIC+Hcno5dMeQEc+B5m/2iMC+PIyovg3bGQmwS3r4BWPewdUZ3YfDx4rfUKrXUHrXWM1vrpqude11q/XnX/Fa11Z611d611v0sld9G87d27l9jYWIYPH15jchcOyMMful0HOz6GfctgxALHT+5gDHg24wtj8LRPrjfa5puJ5nlquQlZuXIlPXr0OGc53RvGUcXHx5OcnMy///1ve4ciLlfCbEBD7EhjDJjGwi8cbv7SGOXy4+ugNN/eETUIhxoPXly+0aNHy0lK0XDCu8Hs1UaPmcbW9TAsHm74ED66Fl4bCOOeN0akbMIa2V9ICGF3Ub0b78TX0UPh9u/Bww8+uwk+vwVOpV9ytcZKErwQonmJ6gN3rjemETy0Cl7pA5vfbJKThkiCF0I0Py5uMOh/YN6vENkLVvwV3h4FGXvsHZlNSYIXQjRfQTEwc4kxreDJFFg0BFYvME7GNgGS4G1o3bp1TJhgswE1HcbSpUt59tlna3x969atMhKkaLyUgu43wPyt0O1G2PgC/LcfJK2xd2R15ri9aL5/BDJ223abLbvC2JoTlaMxm831cpXn5W530qRJTJo0qcbXExISSEio1XUXQjgur0CY8qqR7Jf9GT6cCl2vh9H/D3xC7B3dFZEa/HmOHDlCXFwcc+bMoUuXLtx8882sXr2agQMH0r59ezZv3szmzZsZMGAAV111FQMGDODAgQMXbKdr167k5+ejtSYoKMg6ouLMmTNZvXo1R44cYdCgQfTs2ZOePXtahyNYt24dw4YNY8aMGXTt2hWz2cyDDz5I79696datG2+88UaNsa9bt47BgwczdepU4uPjmTt3rnWwMB8fH5544gn69u3Lb7/9xkcffUSfPn3o0aMHd911l3Wsmh9++IGePXvSvXt3hg8fDsB7773H/PnzAfjyyy/p0qUL3bt3Z/Dgwdb9nv7lkpeXx5QpU+jWrRv9+vVj165dACxYsIA77riDoUOHEh0dzUsvvVTnv5UQ9aLdYKNtfvBDxvyyr/aG7R82zkm9tdZ2WXr16qXPt3fv3guea2gpKSna2dlZ79q1S5vNZt2zZ099++23a4vFopcsWaInT56sCwoKtMlk0lprvWrVKj1t2jSttdZr167V48eP11prfdddd+nly5fr3bt364SEBD1nzhyttdaxsbG6sLBQFxcX69LSUq211gcPHtSnj8fatWu1l5eXTk5O1lpr/cYbb+innnpKa611WVmZ7tWrl/W1861du1a7u7vrpKQkXVlZqUeMGKG//PJLrbXWgP7888+11sZxnjBhgq6oqNBaaz1v3jz9/vvv66ysLB0ZGWndfm5urtZa63fffVffc889Wmutu3TpolNTU7XWWp88efKC9z1//ny9YMECrbXWP/30k+7evbvWWusnn3xS9+/fX5eVlens7GwdGBho3X9NHOHzIJq5zH1avz1a6yf9tH5nnNbZB+0dkQa26lrmWcdtorGjdu3a0bVrVwA6d+7M8OHDUUrRtWtXjhw5QkFBAbfddhuHDh1CKYXJZLpgG4MGDeLnn3+mTZs2zJs3j0WLFpGWlkZgYCA+Pj4UFBRUO147QJ8+fWjXrh0AP/74I7t27bKOTllQUMChQ4esr5+vT58+REdHA3DTTTexceNGpk+fjrOzM9deey0AP/30E9u2bbMOiFZaWkpoaCi///47gwcPtm47MPDC2XAGDhzIrFmzuP76661j3J9t48aNfPXVVwBcc8015ObmUlBQAMD48eNxd3fH3d2d0NBQMjMziYyMvNSfQwj7CY2DWSvgjw9g1RPw2gCj983VfwEXd3tHd0mS4Ktxeux2ACcnJ+tjJycnKisr+dvf/sawYcP45ptvOHLkCEOHDr1gG4MHD+bVV1/l2LFjPP3003zzzTcsXryYQYMGATWP1w7njrOutebll1+u9dWq1Y1lD8YkHqfb3bXW3HbbbTzzzLmz7yxduvSSY86//vrrbNq0ie+++44ePXqwY8eOc17X1fyMPb3Ns4/r2ePiC+HQnJyg1yzoMBZWPgrrnoH0HXDjx8aUhg5M2uCvQEFBAREREYDRPl2dqKgocnJyOHToENHR0Vx99dUsXLjQmuBrGq/9fKNHj+a1116z/ko4ePCgdZ7V6mzevJmUlBQsFguff/55tXO3Dh8+nMWLF5OVlQUY7eZHjx6lf//+rF+/npSUFOvz50tKSqJv37784x//IDg4mOPHzx24afDgwdbx4NetW0dwcDB+fn41xitEo+EbZsxaNfZ5OPg9fP+ww7fLS4K/Ag899BCPPvooAwcOrDExA/Tt25cOHToARpNNWlqaNeHWNF77+ebMmUN8fDw9e/akS5cu3HXXXRet+fbv359HHnmELl260K5du2oHHouPj+ef//wno0aNolu3bowcOZITJ04QEhLCokWLmDZtGt27dz9nSr7THnzwQbp27UqXLl0YPHgw3bt3P+f1BQsWsHXrVrp168YjjzxinXtViCaj750w4D7Y8ib89oq9o7moWo0HXx9kPHjbW7duHQsXLqx2QvDGSD4PwmFZLPDVHUYvm+veM8bIbyCXMx68tMELIcTlcnKCKa/DqRPw9V3gGw6t+9k7qgtIgm+Edu/ezcyZM895zt3dnU2bNlV7wlcIUQ9cPeCmT+GtEfDpjcYwysGx9o7qHJLgG6GuXbte0HtFCGEHXoFwy2J4ayR8fC3M+Qm8g+0dlZWcZBVCiLoIjIYZn0NhJnxyA1SU2DsiK0nwQghRV5EJcO1bkLYNvv6Tw4wtLwleCCFsodMEGPMs7F8OPz5u72gAaYMXQgjb6TcX8o/C7/+FgNbQb55dw5EavA011fHgFyxYwMKFCwGYNWuWdVwcIUQ1Rv0T4ibAD4/CvmV2DcVha/D/2vwv9uftt+k24wLjeLjPwzbdZn2qy3jw9TWWvBDiEpycYdqb8MEk+GoO3LbcmKjcHqHYZa8OrLGPB1/bdZ977jm6du1K9+7deeSRRwB488036d27N927d+faa6+lpMRxegMI0ai4ecFNnxkXQH16A+Ql2yeO2o4rbOtFxoOvn/Hga7PuihUrdP/+/XVxcbHW+sy47zk5OdZtPfbYY/qll17SWhtjuT///PNaa61vu+026xjz9c0RPg9C1En2Ia2fbav1f67Suijn0uVrARkPvm4a+3jwl1p39erV3H777Xh5eQFnxn3fs2cPjz/+OPn5+RQVFdV6iGIhRA2CY42a/PsT4bMZcOu3xhWwDaRWTTRKqTFKqQNKqcNKqUcuUq63UsqslJpuuxAbXm3Hg9+zZw/Lli2jrKzsgm0MHjyYDRs2sGHDBoYOHUpISEiN48Fv3bqViooK67rVjQe/Y8cOduzYQUpKCqNGjaox9tqsq7Wudtz3WbNm8corr7B7926efPLJat+XEOIyte4L096A47/DN3cZA5U1kEsmeKWUM/AqMBaIB25SSsXXUO5fwEpbB+loHHk8+NqsO2rUKN555x1rG/vpcd8LCwsJDw/HZDJZx3QXQthA56lG75q9S2D1kw2229rU4PsAh7XWyVrrCuAzYHI15e4FvgKybBifQ3Lk8eBrs+6YMWOYNGkSCQkJ9OjRw9oF8qmnnqJv376MHDmSuLi4yzkkQohL6T8fev8Jfn0JNr/ZILu85HjwVc0tY7TWc6oezwT6aq3nn1UmAvgEuAZ4G1iutb5oZ2kZD15cinweRJNjMcPXd0LX66DjmCvahK3Hg69uks7zvxVeBB7WWpsvNqenUupO4E6A1q1b1yY+IYRoOpycYfrbDba72iT4VCDqrMeRQPp5ZRKAz6qSezAwTilVqbVecnYhrfUiYBEYNfgrjLnZu9h48EIIcVptEvwWoL1Sqh2QBtwIzDi7gNba2mdPKfUeRhPNkisJqKYeHuKM5jAe/KWaDoUQl3bJk6xa60pgPkbvmH3AF1rrRKXUXKXUXFsG4+HhQW5urvxzN3Naa3Jzc/HwaLj+wkI0RQ416bbJZCI1NVX6Xws8PDyIjIzE1dXV3qEI4VAa7aTbrq6uNV6hKYQQ4vLIYGNCCNFESYIXQogmShK8EEI0UXY7yaqUygaO1vByMJDTgOFcLkePDxw/RomvbiS+umnM8bXRWofUZiN2S/AXo5TaWtuzxPbg6PGB48co8dWNxFc3zSU+aaIRQogmShK8EEI0UY6a4BfZO4BLcPT4wPFjlPjqRuKrm2YRn0O2wQshhKg7R63BCyGEqCNJ8EII0UQ1eIK/1ATeyvBS1eu7lFI9a7uuA8R3RCm1Wym1Qym19fx1Gyi+OKXUb0qpcqXUXy9nXQeIzxGO381Vf9ddSqlflVLda7uuA8TnCMdvclVsO5RSW5VSV9d2XQeIz+7H76xyvZVSZmXMqHdZ655Da91gC+AMJAHRgBuwE4g/r8w44HuMmaT6AZtqu64946t67QgQbOfjFwr0Bp4G/no569ozPgc6fgOAFlX3xzrg56/a+Bzo+Plw5txeN2C/gx2/auNzlON3Vrk1wApgel2OX0PX4Gszgfdk4ANt+B0IUEqF13Jde8bXEC4Zn9Y6S2u9BTBd7rp2jq8h1Ca+X7XWJ6se/o4xg1mt1rVzfA2hNvEV6aqMBHhzZnpPRzl+NcXXEGp7DO4FvgKyrmDdczR0go8Ajp/1OLXqudqUqc269owPjA/Lj0qpbcqYf9bW6nIMHOX4XYyjHb/ZGL/WrmTdK1GX+MBBjp9SaqpSaj/wHXDH5axrx/jAAY6fUioCmAq8frnrVqehx4OvzQTeNZWpzbp1VZf4AAZqrdOVUqHAKqXUfq31zw0cX32sW1t13YfDHD+l1DCMBHq6jdahjl818YGDHD+t9TfAN0qpwcBTwIjarltHdYkPHOP4vQg8rLU2q3OnLr2i49fQNfjaTOBdU5narGvP+NBan77NAr7B+FnV0PHVx7q1Vad9OMrxU0p1A94CJmutcy9nXTvG5zDH76x4fgZilFLBl7uuHeJzlOOXAHymlDoCTAf+q5SaUst1L1RfJxRqOMngAiQD7ThzoqDzeWXGc+5JzM21XdfO8XkDvmfd/xUY09DxnVV2AeeeZHWI43eR+Bzi+AGtgcPAgCt9b3aKz1GOXyxnTmL2BNKq/lcc5fjVFJ9DHL/zyr/HmZOsV3T8bBb8ZbzJccBBjDPCj1U9NxeYW3VfAa9Wvb4bSLjYuo4SH8bZ7Z1VS6Id42uJ8W1/Csivuu/nQMev2vgc6Pi9BZwEdlQtWx3s81dtfA50/B6u2v8O4Dfgagc7ftXG5yjH77yy71GV4K/0+MlQBUII0UTJlaxCCNFESYIXQogmShK8EEI0UZLghRCiiZIEL4QQTZQkeCGEaKIkwQshRBP1/wH/CydwuXgbawAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", "plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", - "\n", "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", - "\n", "# plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", "plt.legend()" diff --git a/Model Training/XGBoost_with_hash.ipynb b/Model Training/XGBoost_with_hash.ipynb index ad8afad..ddcdeb9 100644 --- a/Model Training/XGBoost_with_hash.ipynb +++ b/Model Training/XGBoost_with_hash.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -79,29 +79,33 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", + "# covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", + "\n", + "X = pd.read_csv('../data/Training_Data/Training_Dataset_with_threshold.csv')\n", "repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", - "covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')" + "covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "y = pd.read_csv('../data/Training_Data/Labels.csv')\n", - "repackaged_benign_test_y = pd.read_csv('../data/Test_Data/Labels_Repackaged_Benign_Test.csv')\n", - "COVID_test_y = pd.read_csv('../data/Test_Data/Labels_COVID_Test.csv')" + "y = pd.read_csv('../data/Training_Data/Labels_trainingset.csv')\n", + "repackaged_benign_test_y = pd.read_csv('../data/Test_Data/Labels_testset.csv')\n", + "COVID_test_y = pd.read_csv('../data/Test_Data/Labels_COVID_testset.csv')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -132,16 +136,27 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Series([], Name: count, dtype: float64)" + "Permission: FACTORY_TEST 9538.0\n", + "Permission: DUMP 9538.0\n", + "Permission: BATTERY_STATS 9538.0\n", + "Permission: BIND_WALLPAPER 9538.0\n", + "Permission: BIND_INPUT_METHOD 9538.0\n", + " ... \n", + "step_detector 9538.0\n", + "temperature 9538.0\n", + "if_the_app_using_suspicious_libs 9538.0\n", + "malwareNum 9538.0\n", + "proportion 9538.0\n", + "Name: count, Length: 866, dtype: float64" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -159,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -169,6 +184,26 @@ "covid_test_X.drop(drop_features, axis = 1, inplace = True)" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "531" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(drop_features)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -184,7 +219,7 @@ "source": [ "X['label']=y['label']\n", "# Create dataset without sensor features in order to see if there's some improvement by adding sensor features\n", - "sensor_lst=list(X.iloc[:,-32:-3].columns)\n", + "sensor_lst=list(X.iloc[:,-14:-3].columns)\n", "# sensor_lst.remove('if_the_app_using_suspicious_libs')\n", "X_wo_sensors = X.drop(sensor_lst, axis = 1)\n", "\n", @@ -196,6 +231,36 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['accelerometer',\n", + " 'gravity',\n", + " 'gyroscope',\n", + " 'light',\n", + " 'linear_acceleration',\n", + " 'magnetic_field',\n", + " 'orientation',\n", + " 'proximity',\n", + " 'rotation_vector',\n", + " 'temperature',\n", + " 'if_the_app_using_suspicious_libs']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sensor_lst" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "# Train test split\n", @@ -219,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -275,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -284,25 +349,26 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The mean test score of XGBoost model with sensor features is: 0.8729662145476617\n" + "The mean test score of XGBoost model with sensor features is: 0.8787996882307093\n" ] } ], "source": [ - "avg_val_score = np.mean(cross_val_score(estimator, X_train_resample.drop(['proportion','label'],axis=1), np.squeeze(y_train_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", + "# avg_val_score = np.mean(cross_val_score(estimator, X_train_resample.drop(['proportion','label'],axis=1), np.squeeze(y_train_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", + "avg_val_score = np.mean(cross_val_score(estimator, X_train_resample.drop(['malwareNum','label'],axis=1), np.squeeze(y_train_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", "print('The mean test score of XGBoost model with sensor features is:', avg_val_score)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -314,7 +380,9 @@ } ], "source": [ - "avg_val_score = np.mean(cross_val_score(estimator, X_train_wo_sensor_resample.drop(['proportion','label'],axis=1), np.squeeze(y_train_wo_sensor_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", + "# avg_val_score = np.mean(cross_val_score(estimator, X_train_wo_sensor_resample.drop(['proportion','label'],axis=1), np.squeeze(y_train_wo_sensor_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", + "\n", + "avg_val_score = np.mean(cross_val_score(estimator, X_train_wo_sensor_resample.drop(['malwareNum','label'],axis=1), np.squeeze(y_train_wo_sensor_resample.to_numpy().reshape(1, -1)), cv = 5, scoring = 'balanced_accuracy'))\n", "print('The mean test score of XGBoost model without sensor features is:', avg_val_score)" ] }, @@ -392,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -413,10 +481,16 @@ " objective='binary:logistic',\n", " seed = seed)\n", " # Fit the estimator\n", - " estimator_function.fit(X_train_resample.drop(['proportion','label'],axis=1),X_train_resample['label'])\n", + "# estimator_function.fit(X_train_resample.drop(['proportion','label'],axis=1),X_train_resample['label'])\n", + " estimator_function.fit(X_train_resample.drop(['malwareNum','proportion','label'],axis=1),X_train_resample['label'])\n", + " \n", + " \n", " \n", " # calculate out-of-the-box roc_score using validation set 1\n", - " probs = estimator_function.predict_proba(X_eval.drop(['proportion','label'],axis=1))\n", + "# probs = estimator_function.predict_proba(X_eval.drop(['proportion','label'],axis=1))\n", + " probs = estimator_function.predict_proba(X_eval.drop(['malwareNum','proportion','label'],axis=1))\n", + " \n", + " \n", " probs = probs[:,1]\n", " val1_roc = roc_auc_score(y_eval,probs)\n", " \n", @@ -426,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -448,10 +522,10 @@ " objective='binary:logistic',\n", " seed = seed)\n", " # Fit the estimator\n", - " estimator_function.fit(X_train_wo_sensor_resample.drop(['proportion','label'],axis=1),X_train_wo_sensor_resample['label'])\n", + " estimator_function.fit(X_train_wo_sensor_resample.drop(['proportion','malwareNum','label'],axis=1),X_train_wo_sensor_resample['label'])\n", " \n", " # calculate out-of-the-box roc_score using validation set 1\n", - " probs = estimator_function.predict_proba(X_eval_wo_sensor.drop(['proportion','label'],axis=1))\n", + " probs = estimator_function.predict_proba(X_eval_wo_sensor.drop(['proportion','malwareNum','label'],axis=1))\n", " probs = probs[:,1]\n", " val1_roc = roc_auc_score(y_eval_wo_sensor,probs)\n", " \n", @@ -461,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -470,29 +544,29 @@ "text": [ "| iter | target | learni... | max_depth | n_esti... | reg_alpha |\n", "-------------------------------------------------------------------------\n", - "| \u001b[0m 1 \u001b[0m | \u001b[0m 0.9466 \u001b[0m | \u001b[0m 0.3751 \u001b[0m | \u001b[0m 17.81 \u001b[0m | \u001b[0m 95.5 \u001b[0m | \u001b[0m 0.07568 \u001b[0m |\n", - "| \u001b[95m 2 \u001b[0m | \u001b[95m 0.9542 \u001b[0m | \u001b[95m 0.7769 \u001b[0m | \u001b[95m 21.65 \u001b[0m | \u001b[95m 14.93 \u001b[0m | \u001b[95m 0.8177 \u001b[0m |\n", - "| \u001b[0m 3 \u001b[0m | \u001b[0m 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"stdout", "output_type": "stream", "text": [ - "{'target': 0.9582351434426231, 'params': {'learning_rate': 0.28729719343540316, 'max_depth': 22.71785408959317, 'n_estimators': 14.904823930846312, 'reg_alpha': 0.9535908701007884}}\n" + "{'target': 0.974735696517413, 'params': {'learning_rate': 0.11303958411260817, 'max_depth': 17.968946448214748, 'n_estimators': 65.73295322863495, 'reg_alpha': 0.3079653289036747}}\n" ] } ], @@ -554,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -563,29 +637,29 @@ "text": [ "| iter | target | learni... | max_depth | n_esti... | reg_alpha |\n", "-------------------------------------------------------------------------\n", - "| \u001b[0m 1 \u001b[0m | \u001b[0m 0.9486 \u001b[0m | \u001b[0m 0.3751 \u001b[0m | \u001b[0m 17.81 \u001b[0m | \u001b[0m 95.5 \u001b[0m | \u001b[0m 0.07568 \u001b[0m |\n", - "| \u001b[95m 2 \u001b[0m | \u001b[95m 0.9539 \u001b[0m | \u001b[95m 0.7769 \u001b[0m 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+699,19 @@ "# kappa is a measure of 'aggressiveness' of the bayesian optimization process\n", "# The algorithm will randomly choose 3 points to establish a 'prior', then will perform \n", "# 10 interations to maximize the value of estimator function\n", - "xgbcBO_wo.maximize(init_points=3,n_iter=20,acq='ucb', kappa= 3, **gp_params)\n" + "xgbcBO_wo.maximize(init_points=3,n_iter=20,acq='ucb', kappa= 3, **gp_params)" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'target': 0.9573044740437158, 'params': {'learning_rate': 0.4654869515316642, 'max_depth': 21.127970235007517, 'n_estimators': 11.435220242864505, 'reg_alpha': 0.9373015690531954}}\n" + "{'target': 0.9696050995024875, 'params': {'learning_rate': 0.3402860424080705, 'max_depth': 21.608011621945234, 'n_estimators': 14.66211514786415, 'reg_alpha': 0.8880013815034622}}\n" ] } ], @@ -654,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -669,28 +743,30 @@ " print(param_dct)\n", " continuous = make_column_selector(dtype_exclude = 'object')\n", " model = xgb.XGBClassifier(**param_dct)\n", - " model.fit(X_train.drop(['proportion','label'],axis=1), np.squeeze(y_train.to_numpy().reshape(1, -1)))\n", + "# model.fit(X_train.drop(['proportion','label'],axis=1), np.squeeze(y_train.to_numpy().reshape(1, -1)))\n", + " model.fit(X_train, np.squeeze(y_train.to_numpy().reshape(1, -1)))\n", " y_pred = model.predict_proba(X_test)\n", " return model,y_pred" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "{'learning_rate': 0.4654869515316642, 'max_depth': 21, 'n_estimators': 11, 'reg_alpha': 0.9373015690531954}\n" + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "{'learning_rate': 0.3402860424080705, 'max_depth': 21, 'n_estimators': 14, 'reg_alpha': 0.8880013815034622}\n" ] } ], "source": [ - "pipe_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample,y_train_resample,X_test.drop(['proportion','label'],axis=1),y_test)\n", - "pipe_without_sensor,y_pred_wo_sensor = model_create_fit(xgbcBO_wo,X_train_wo_sensor_resample,y_train_wo_sensor_resample,X_test_wo_sensor.drop(['proportion','label'],axis=1),y_test_wo_sensor)" + "pipe_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample.drop(['malwareNum','proportion','label'],axis=1),y_train_resample,X_test.drop(['malwareNum','proportion','label'],axis=1),y_test)\n", + "pipe_without_sensor,y_pred_wo_sensor = model_create_fit(xgbcBO_wo,X_train_wo_sensor_resample.drop(['malwareNum','proportion','label'],axis=1),y_train_wo_sensor_resample,X_test_wo_sensor.drop(['malwareNum','proportion','label'],axis=1),y_test_wo_sensor)\n", + "\n" ] }, { @@ -746,7 +822,58 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AUC (including sensor features) 0.9630916074114648\n", + "AUC (not including sensor features) 0.9517960242062395\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (10, 6))\n", + "fpr, tpr, _ = roc_curve(y_test, y_pred_with_sensor[:, 1])\n", + "fpr2, tpr2, _2 = roc_curve(y_test_wo_sensor, y_pred_wo_sensor[:, 1])\n", + "plt.plot(fpr, tpr, lw=2, label='With sensor features')\n", + "plt.plot(fpr2, tpr2, lw=2, label='Without sensor features')\n", + "print('AUC (including sensor features)', roc_auc_score(y_test, y_pred_with_sensor[:, 1]))\n", + "print('AUC (not including sensor features)', roc_auc_score(y_test, y_pred_wo_sensor[:, 1]))\n", + "\n", + "plt.xlabel('False Positive Rate', fontsize = 13)\n", + "plt.ylabel('True Positive Rate', fontsize = 13)\n", + "plt.title('ROC Curve', fontsize = 15)\n", + "plt.legend(fontsize = 13)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -758,19 +885,19 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([0.63293651, 0.95249131]),\n", - " array([0.79551122, 0.89885183]),\n", - " array([0.70497238, 0.92489451]),\n", - " array([ 401, 1829]))" + "(array([0.74318182, 0.94959128]),\n", + " array([0.81546135, 0.92501659]),\n", + " array([0.77764566, 0.93714286]),\n", + " array([ 401, 1507]))" ] }, - "execution_count": 98, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -781,19 +908,19 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([0.62173038, 0.94691287]),\n", - " array([0.77057357, 0.89721159]),\n", - " array([0.68819599, 0.92139248]),\n", - " array([ 401, 1829]))" + "(array([0.72321429, 0.94726027]),\n", + " array([0.80798005, 0.91771732]),\n", + " array([0.76325088, 0.9322548 ]),\n", + " array([ 401, 1507]))" ] }, - "execution_count": 99, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -818,15 +945,15 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Balanced Average Accuracy (including sensor features): 0.8471815267735527\n", - "Balanced Average Accuracy (not including sensor features): 0.8338925785590698\n" + "Balanced Average Accuracy (including sensor features): 0.8702389679417912\n", + "Balanced Average Accuracy (not including sensor features): 0.8628486845262424\n" ] } ], @@ -844,7 +971,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -853,13 +980,17 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# Train test split\n", - "X_ = pd.read_csv('../data/Training_Data/Training_Dataset.csv')\n", - "y_ = pd.read_csv('../data/Training_Data/Labels.csv')\n", + "X_ = pd.read_csv('../data/Training_Data/Training_Dataset_with_threshold.csv')\n", + "y_ = pd.read_csv('../data/Training_Data/Labels_trainingset.csv')\n", + "\n", + "# X = pd.read_csv('../data/Training_Data/Training_Dataset_with_ratio.csv')\n", + "# repackaged_benign_test_X = pd.read_csv('../data/Test_Data/Repackaged_Benign_Testset.csv')\n", + "# covid_test_X = pd.read_csv('../data/Test_Data/COVID_Testset.csv')\n", "\n", "X_train_, X_test_, y_train_, y_test_ = train_test_split(X_,y_['label'], \n", " test_size = 0.2, \n", @@ -869,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -906,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -986,45 +1117,22 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy for paired apps (including sensor features): 0.8502202643171806\n", - "Accuracy for non-paired apps (including sensor features): 0.8662006989515726\n", - "Accuracy for paired apps (not including sensor features): 0.8325991189427313\n", - "Accuracy for non-paired apps (not including sensor features): 0.8627059410883674\n" - ] - } - ], - "source": [ - "# print(\"Accuracy for paired apps (including sensor features):\", np.sum(paired_w_sensor_accuracy)/len(paired_w_sensor_accuracy))\n", - "# print(\"Accuracy for non-paired apps (including sensor features):\", np.sum(non_paired_w_sensor_accuracy)/len(non_paired_w_sensor_accuracy))\n", - "\n", - "# print(\"Accuracy for paired apps (not including sensor features):\", np.sum(paired_wo_sensor_accuracy)/len(paired_wo_sensor_accuracy))\n", - "# print(\"Accuracy for non-paired apps (not including sensor features):\", np.sum(non_paired_wo_sensor_accuracy)/len(non_paired_wo_sensor_accuracy))" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy for paired apps (including sensor features): 0.8414096916299559\n", - "Accuracy for non-paired apps (including sensor features): 0.8846729905142287\n", - "Accuracy for paired apps (not including sensor features): 0.8370044052863436\n", - "Accuracy for non-paired apps (not including sensor features): 0.8786819770344483\n" + "Accuracy for paired apps (including sensor features): 0.8819875776397516\n", + "Accuracy for non-paired apps (including sensor features): 0.9038351459645106\n", + "Accuracy for paired apps (not including sensor features): 0.8571428571428571\n", + "Accuracy for non-paired apps (not including sensor features): 0.8981110475100171\n" ] } ], "source": [ + "## update\n", "print(\"Accuracy for paired apps (including sensor features):\", np.sum(paired_w_sensor_accuracy)/len(paired_w_sensor_accuracy))\n", "print(\"Accuracy for non-paired apps (including sensor features):\", np.sum(non_paired_w_sensor_accuracy)/len(non_paired_w_sensor_accuracy))\n", "\n", @@ -1069,16 +1177,16 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2858, 324)" + "(2858, 322)" ] }, - "execution_count": 105, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1089,7 +1197,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1099,7 +1207,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1108,14 +1216,14 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy (including sensor features): 0.8400979706088173\n" + "Accuracy (including sensor features): 0.8908327501749476\n" ] } ], @@ -1125,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1134,14 +1242,14 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy (without sensor features): 0.8512946116165151\n" + "Accuracy (without sensor features): 0.9748075577326802\n" ] } ], @@ -1201,7 +1309,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1212,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1246,72 +1354,72 @@ " \n", " 0\n", " 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81\n", - " 162\n", - " 118\n", - " 0.728395\n", + " 111\n", + " 85\n", + " 0.765766\n", " \n", " \n", " 1\n", - " 28EAC321D548B4247D9C84810C0656EC9426716B\n", - " 97\n", - " 82\n", - " 0.845361\n", - " \n", - " \n", - " 2\n", " F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284\n", " 71\n", " 71\n", " 1.000000\n", " \n", " \n", - " 3\n", + " 2\n", " 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03\n", - " 67\n", - " 67\n", + " 55\n", + " 55\n", " 1.000000\n", " \n", " \n", + " 3\n", + " 28EAC321D548B4247D9C84810C0656EC9426716B\n", + " 54\n", + " 39\n", + " 0.722222\n", + " \n", + " \n", " 4\n", - " 00F7DCC41988D8642C51D4F8BA5A42C413275885\n", - " 58\n", - " 58\n", + " 45A195BE1E17B3AFA086623DCC4661DEE2043B70\n", + " 51\n", + " 51\n", " 1.000000\n", " \n", " \n", " 5\n", " F243B92AD5EABA98BD43084864C4D5483F191CD9\n", - " 52\n", - " 44\n", - " 0.846154\n", + " 42\n", + " 32\n", + " 0.761905\n", " \n", " \n", " 6\n", - " 45A195BE1E17B3AFA086623DCC4661DEE2043B70\n", - " 45\n", - " 45\n", + " 00F7DCC41988D8642C51D4F8BA5A42C413275885\n", + " 40\n", + " 40\n", " 1.000000\n", " \n", " \n", " 7\n", " 6C699C8D1F7157366994ACDA5495051F2C58D7AB\n", - " 44\n", - " 44\n", + " 38\n", + " 38\n", " 1.000000\n", " \n", " \n", " 8\n", - " 699512C8B49E7A01A622BD250544E09A80A42D55\n", - " 39\n", - " 34\n", - " 0.871795\n", + " 5BFC55F389F4B5427341E4320A501711140AE444\n", + " 36\n", + " 24\n", + " 0.666667\n", " \n", " \n", " 9\n", - " 5BFC55F389F4B5427341E4320A501711140AE444\n", - " 35\n", - " 19\n", - " 0.542857\n", + " 699512C8B49E7A01A622BD250544E09A80A42D55\n", + " 33\n", + " 26\n", + " 0.787879\n", " \n", " \n", "\n", @@ -1319,19 +1427,19 @@ ], "text/plain": [ " ThumbPrint N correct score\n", - "0 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81 162 118 0.728395\n", - "1 28EAC321D548B4247D9C84810C0656EC9426716B 97 82 0.845361\n", - "2 F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284 71 71 1.000000\n", - "3 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03 67 67 1.000000\n", - "4 00F7DCC41988D8642C51D4F8BA5A42C413275885 58 58 1.000000\n", - "5 F243B92AD5EABA98BD43084864C4D5483F191CD9 52 44 0.846154\n", - "6 45A195BE1E17B3AFA086623DCC4661DEE2043B70 45 45 1.000000\n", - "7 6C699C8D1F7157366994ACDA5495051F2C58D7AB 44 44 1.000000\n", - "8 699512C8B49E7A01A622BD250544E09A80A42D55 39 34 0.871795\n", - "9 5BFC55F389F4B5427341E4320A501711140AE444 35 19 0.542857" + "0 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81 111 85 0.765766\n", + "1 F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284 71 71 1.000000\n", + "2 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03 55 55 1.000000\n", + "3 28EAC321D548B4247D9C84810C0656EC9426716B 54 39 0.722222\n", + "4 45A195BE1E17B3AFA086623DCC4661DEE2043B70 51 51 1.000000\n", + "5 F243B92AD5EABA98BD43084864C4D5483F191CD9 42 32 0.761905\n", + "6 00F7DCC41988D8642C51D4F8BA5A42C413275885 40 40 1.000000\n", + "7 6C699C8D1F7157366994ACDA5495051F2C58D7AB 38 38 1.000000\n", + "8 5BFC55F389F4B5427341E4320A501711140AE444 36 24 0.666667\n", + "9 699512C8B49E7A01A622BD250544E09A80A42D55 33 26 0.787879" ] }, - "execution_count": 113, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1348,7 +1456,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1382,72 +1490,72 @@ " \n", " 0\n", " 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81\n", - " 162\n", - " 122\n", - " 0.753086\n", - " \n", - " \n", - " 1\n", - " 28EAC321D548B4247D9C84810C0656EC9426716B\n", - " 97\n", + " 111\n", " 80\n", - " 0.824742\n", + " 0.720721\n", " \n", " \n", - " 2\n", + " 1\n", " F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284\n", " 71\n", " 71\n", " 1.000000\n", " \n", " \n", - " 3\n", + " 2\n", " 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03\n", - " 67\n", - " 67\n", + " 55\n", + " 55\n", " 1.000000\n", " \n", " \n", + " 3\n", + " 28EAC321D548B4247D9C84810C0656EC9426716B\n", + " 54\n", + " 39\n", + " 0.722222\n", + " \n", + " \n", " 4\n", - " 00F7DCC41988D8642C51D4F8BA5A42C413275885\n", - " 58\n", - " 58\n", + " 45A195BE1E17B3AFA086623DCC4661DEE2043B70\n", + " 51\n", + " 51\n", " 1.000000\n", " \n", " \n", " 5\n", " F243B92AD5EABA98BD43084864C4D5483F191CD9\n", - " 52\n", - " 45\n", - " 0.865385\n", + " 42\n", + " 32\n", + " 0.761905\n", " \n", " \n", " 6\n", - " 45A195BE1E17B3AFA086623DCC4661DEE2043B70\n", - " 45\n", - " 45\n", + " 00F7DCC41988D8642C51D4F8BA5A42C413275885\n", + " 40\n", + " 40\n", " 1.000000\n", " \n", " \n", " 7\n", " 6C699C8D1F7157366994ACDA5495051F2C58D7AB\n", - " 44\n", - " 44\n", + " 38\n", + " 38\n", " 1.000000\n", " \n", " \n", " 8\n", - " 699512C8B49E7A01A622BD250544E09A80A42D55\n", - " 39\n", - " 35\n", - " 0.897436\n", + " 5BFC55F389F4B5427341E4320A501711140AE444\n", + " 36\n", + " 24\n", + " 0.666667\n", " \n", " \n", " 9\n", - " 5BFC55F389F4B5427341E4320A501711140AE444\n", - " 35\n", - " 17\n", - " 0.485714\n", + " 699512C8B49E7A01A622BD250544E09A80A42D55\n", + " 33\n", + " 27\n", + " 0.818182\n", " \n", " \n", "\n", @@ -1455,19 +1563,19 @@ ], "text/plain": [ " ThumbPrint N correct score\n", - "0 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81 162 122 0.753086\n", - "1 28EAC321D548B4247D9C84810C0656EC9426716B 97 80 0.824742\n", - "2 F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284 71 71 1.000000\n", - "3 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03 67 67 1.000000\n", - "4 00F7DCC41988D8642C51D4F8BA5A42C413275885 58 58 1.000000\n", - "5 F243B92AD5EABA98BD43084864C4D5483F191CD9 52 45 0.865385\n", - "6 45A195BE1E17B3AFA086623DCC4661DEE2043B70 45 45 1.000000\n", - "7 6C699C8D1F7157366994ACDA5495051F2C58D7AB 44 44 1.000000\n", - "8 699512C8B49E7A01A622BD250544E09A80A42D55 39 35 0.897436\n", - "9 5BFC55F389F4B5427341E4320A501711140AE444 35 17 0.485714" + "0 61ED377E85D386A8DFEE6B864BD85B0BFAA5AF81 111 80 0.720721\n", + "1 F3FA0BE3FEB31AC2920E399AF0F0CFB37D729284 71 71 1.000000\n", + "2 95653B2BD67722B6EF4021CB812FF2B4DC5DCF03 55 55 1.000000\n", + "3 28EAC321D548B4247D9C84810C0656EC9426716B 54 39 0.722222\n", + "4 45A195BE1E17B3AFA086623DCC4661DEE2043B70 51 51 1.000000\n", + "5 F243B92AD5EABA98BD43084864C4D5483F191CD9 42 32 0.761905\n", + "6 00F7DCC41988D8642C51D4F8BA5A42C413275885 40 40 1.000000\n", + "7 6C699C8D1F7157366994ACDA5495051F2C58D7AB 38 38 1.000000\n", + "8 5BFC55F389F4B5427341E4320A501711140AE444 36 24 0.666667\n", + "9 699512C8B49E7A01A622BD250544E09A80A42D55 33 27 0.818182" ] }, - "execution_count": 114, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1521,7 +1629,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -2740,11 +2848,11 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "def set_label(X,thres):\n", + "def set_label_ratio(X,thres):\n", " X_tp=X\n", " X_tp['label']=0\n", " X_tp.loc[X_tp['proportion']>=thres,'label']=1\n", @@ -2752,6 +2860,20 @@ " return X_tp" ] }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "def set_label_count(X,thres):\n", + " X_tp=X.copy()\n", + " X_tp['label']=0\n", + " X_tp.loc[X_tp['malwareNum']>=thres,'label']=1\n", + " print(sum(X_tp['label'])/X_tp.shape[0])\n", + " return X_tp" + ] + }, { "cell_type": "code", "execution_count": 36, @@ -2820,22 +2942,90 @@ "source": [ "for thres in list(np.arange(0.05,0.36,0.02)):\n", " print('------thres=',thres)\n", - " set_label(X_train,thres)\n", - " set_label(X_test,thres)" + " set_label_ratio(X_train,thres)\n", + " set_label_ratio(X_test,thres)" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------thres= 2\n", + "0.7994616419919246\n", + "0.8031390134529148\n", + "------thres= 3\n", + "0.7558322117541498\n", + "0.7488789237668162\n", + "------thres= 4\n", + "0.730820995962315\n", + "0.7260089686098655\n", + "------thres= 5\n", + "0.7161283086585913\n", + "0.7107623318385651\n", + "------thres= 6\n", + "0.7028936742934051\n", + "0.6995515695067265\n", + "------thres= 7\n", + "0.6882009869896815\n", + "0.6860986547085202\n", + "------thres= 8\n", + "0.677321668909825\n", + "0.6775784753363229\n", + "------thres= 9\n", + "0.6609466128308659\n", + "0.6614349775784754\n", + "------thres= 10\n", + "0.6325706594885598\n", + "0.6358744394618834\n", + "------thres= 11\n", + "0.5944369672498878\n", + "0.5968609865470852\n", + "------thres= 12\n", + "0.5503589053387169\n", + "0.5506726457399103\n", + "------thres= 13\n", + "0.5112157918349035\n", + "0.515695067264574\n", + "------thres= 14\n", + "0.4669134140870345\n", + "0.47399103139013454\n", + "------thres= 15\n", + "0.4205921938088829\n", + "0.41838565022421526\n" + ] + } + ], + "source": [ + "for thres in list(range(2,16,1)):\n", + " print('------thres=',thres)\n", + " set_label_count(X_train,thres)\n", + " set_label_count(X_test,thres)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ - "def compute_metric_thres(X_train,X_test,thres):\n", - " X_train_tp=set_label(X_train,thres)\n", - " X_test_tp=set_label(X_test,thres)\n", + "def compute_metric_thres(X_train,X_test,thres,drop_lst=[],mode=1):\n", + " if mode==1:\n", + " X_train_tp=set_label_ratio(X_train,thres)\n", + " X_test_tp=set_label_ratio(X_test,thres)\n", + " else:\n", + " X_train_tp=set_label_count(X_train,thres)\n", + " X_test_tp=set_label_count(X_test,thres)\n", " X_train_resample_tp,y_train_resample_tp=resample(X_train_tp)\n", " \n", - " model_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample_tp,y_train_resample_tp,X_test_tp.drop(['proportion','label'],axis=1),X_test_tp['label'])\n", + "# model_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample_tp,y_train_resample_tp,X_test_tp.drop(['proportion','label'],axis=1),X_test_tp['label'])\n", + " model_with_sensor,y_pred_with_sensor = model_create_fit(xgbcBO,X_train_resample_tp.drop(drop_lst,axis=1),y_train_resample_tp,X_test_tp.drop(drop_lst,axis=1),X_test_tp['label'])\n", + " \n", + "\n", " score=balanced_accuracy_score(X_test_tp['label'], np.argmax(y_pred_with_sensor,axis=1))\n", "# print('Balanced Average Accuracy (including sensor features):', score)\n", " metrics=precision_recall_fscore_support(X_test_tp['label'], np.argmax(y_pred_with_sensor,axis=1))\n", @@ -2848,7 +3038,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -2856,105 +3046,105 @@ "output_type": "stream", "text": [ "------thres= 0.01\n", - "0.8087707492148946\n", - "0.8103139013452915\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8548519613114746 recall_benign=0.8037825059101655 recall_malware=0.9059214167127836\n", + "0.7897771952817825\n", + "0.789832285115304\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.886871242596892 recall_benign=0.8553615960099751 recall_malware=0.9183808891838089\n", "------thres= 0.03\n", - "0.7994616419919246\n", - "0.8031390134529148\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8591241451499461 recall_benign=0.8109339407744874 recall_malware=0.9073143495254048\n", + "0.7896461336828309\n", + "0.789308176100629\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.873178926086698 recall_benign=0.8233830845771144 recall_malware=0.9229747675962815\n", "------thres= 0.049999999999999996\n", - "0.7520188425302826\n", - "0.7457399103139013\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8590412134208486 recall_benign=0.8112874779541446 recall_malware=0.9067949488875526\n", + "0.7870249017038008\n", + "0.7861635220125787\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8808921568627451 recall_benign=0.8357843137254902 recall_malware=0.926\n", "------thres= 0.06999999999999999\n", - "0.7239793629430238\n", - "0.7197309417040358\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.861582554517134 recall_benign=0.816 recall_malware=0.907165109034268\n", + "0.7815203145478374\n", + "0.7809224318658281\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8766706271474904 recall_benign=0.8325358851674641 recall_malware=0.9208053691275168\n", "------thres= 0.08999999999999998\n", - "0.7095109914759982\n", - "0.7053811659192825\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8591654643958504 recall_benign=0.806697108066971 recall_malware=0.9116338207247299\n", + "0.7745740498034076\n", + "0.7772536687631028\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8761699258260283 recall_benign=0.84 recall_malware=0.9123398516520567\n", "------thres= 0.10999999999999997\n", - "0.6970614625392553\n", - "0.6937219730941704\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8562456405019492 recall_benign=0.8023426061493412 recall_malware=0.9101486748545572\n", + "0.7623853211009174\n", + "0.760482180293501\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8553566769767171 recall_benign=0.8030634573304157 recall_malware=0.9076498966230186\n", "------thres= 0.12999999999999998\n", - "0.6791161956034096\n", - "0.6784753363228699\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8541616543190075 recall_benign=0.793584379358438 recall_malware=0.914738929279577\n", + "0.7428571428571429\n", + "0.7389937106918238\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.842805263607622 recall_benign=0.7891566265060241 recall_malware=0.8964539007092198\n", "------thres= 0.15\n", - "0.65814266487214\n", - "0.657847533632287\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8541209358173393 recall_benign=0.7798165137614679 recall_malware=0.9284253578732107\n", + "0.7193971166448231\n", + "0.720125786163522\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8223427048067644 recall_benign=0.7640449438202247 recall_malware=0.8806404657933042\n", "------thres= 0.16999999999999998\n", - "0.6087931807985644\n", - "0.6062780269058295\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8232509670984351 recall_benign=0.744874715261959 recall_malware=0.9016272189349113\n", + "0.6657929226736566\n", + "0.6645702306079665\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8057940654574133 recall_benign=0.7921875 recall_malware=0.8194006309148265\n", "------thres= 0.18999999999999997\n", - "0.5584342754598475\n", - "0.5569506726457399\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.843597403952095 recall_benign=0.8208502024291497 recall_malware=0.8663446054750402\n", + "0.608781127129751\n", + "0.6158280922431866\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8135816086615772 recall_benign=0.7803547066848567 recall_malware=0.8468085106382979\n", "------thres= 0.20999999999999996\n", - "0.5163750560789592\n", - "0.5224215246636771\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8451449757198413 recall_benign=0.8018779342723005 recall_malware=0.8884120171673819\n", + "0.5636959370904325\n", + "0.5718029350104822\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8322550028215723 recall_benign=0.7882496940024479 recall_malware=0.8762603116406966\n", "------thres= 0.22999999999999998\n", - "0.4728577837595334\n", - "0.4780269058295964\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8423894121970561 recall_benign=0.7757731958762887 recall_malware=0.9090056285178236\n", + "0.5174311926605505\n", + "0.5178197064989518\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8099432318253829 recall_benign=0.7271739130434782 recall_malware=0.8927125506072875\n", "------thres= 0.24999999999999997\n", - "0.4329295648272768\n", - "0.437219730941704\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8252242312800082 recall_benign=0.7314741035856573 recall_malware=0.918974358974359\n", + "0.4748361730013106\n", + "0.470125786163522\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7843355199825333 recall_benign=0.6745796241345203 recall_malware=0.8940914158305463\n", "------thres= 0.26999999999999996\n", - "0.3566621803499327\n", - "0.34798206278026905\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.7929662573207221 recall_benign=0.6568088033012379 recall_malware=0.9291237113402062\n", + "0.3871559633027523\n", + "0.3731656184486373\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7480341775957311 recall_benign=0.6070234113712375 recall_malware=0.8890449438202247\n", "------thres= 0.29\n", - "0.2711978465679677\n", - "0.26905829596412556\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.7805163599182003 recall_benign=0.8226993865030675 recall_malware=0.7383333333333333\n", + "0.2916120576671035\n", + "0.28354297693920333\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7438364295981188 recall_benign=0.8222384784198976 recall_malware=0.6654343807763401\n", "------thres= 0.30999999999999994\n", - "0.20592193808882908\n", - "0.20134529147982064\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8147127123847492 recall_benign=0.8298708590679393 recall_malware=0.799554565701559\n", + "0.21874180865006554\n", + "0.21016771488469602\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7706869190659715 recall_benign=0.8181818181818182 recall_malware=0.7231920199501247\n", "------thres= 0.32999999999999996\n", - "0.1594885598923284\n", - "0.16322869955156952\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8288841383696689 recall_benign=0.8665594855305466 recall_malware=0.7912087912087912\n", + "0.17116644823066843\n", + "0.15828092243186584\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7932545998861884 recall_benign=0.8580323785803238 recall_malware=0.7284768211920529\n", "------thres= 0.35\n", - "0.1320098698968147\n", - "0.13632286995515694\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8398764141662568 recall_benign=0.8738317757009346 recall_malware=0.805921052631579\n", + "0.1436435124508519\n", + "0.1278825995807128\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8015989517654476 recall_benign=0.8695913461538461 recall_malware=0.7336065573770492\n", "------thres= 0.36999999999999994\n", - "0.1078959174517721\n", - "0.10582959641255606\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8452327320945888 recall_benign=0.8811434302908726 recall_malware=0.809322033898305\n", + "0.11651376146788991\n", + "0.1090146750524109\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8080316742081448 recall_benign=0.8852941176470588 recall_malware=0.7307692307692307\n", "------thres= 0.38999999999999996\n", - "0.08770749214894571\n", - "0.08475336322869956\n", - "{'learning_rate': 0.28729719343540316, 'max_depth': 22, 'n_estimators': 14, 'reg_alpha': 0.9535908701007884}\n", - "balanced acc=0.8474863188238984 recall_benign=0.8907398334149926 recall_malware=0.8042328042328042\n" + "0.09541284403669725\n", + "0.09014675052410902\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8019169971064195 recall_benign=0.9003456221198156 recall_malware=0.7034883720930233\n" ] } ], @@ -2963,7 +3153,7 @@ "pd_metric=collections.defaultdict(list)\n", "for thres in list(np.arange(0.01,0.40,0.02)):\n", " print('------thres=',thres)\n", - " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train,X_test,thres)\n", + " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train.drop('malwareNum',axis=1),X_test.drop('malwareNum',axis=1),thres,['proportion','label'],1)\n", " pd_metric['thres'].append(thres)\n", " pd_metric['balanced_accuracy'].append(score)\n", " pd_metric['benign_recall'].append(recall1)\n", @@ -2974,7 +3164,257 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(pd_metric).to_csv('../../xgboost_threshold.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.DataFrame(pd_metric)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MSgydfDrxYpsXaenV0tymVXkq3xMuY+PVCHq+DhP2QpMBWubOri/MbVWZsLXWMfvJNrSq68bkXw6z79wNc5tUKr756yxHo5L4cEhQlSs68nHVZvTRSdWvV62Ukk2Rmxjy+xCm751OTYea/NDvB37o94Ny8hWEcvR52DjA8J+0mP3W/8KWd7Tc/EqGo60180e3p56HI88sCiXiSpK5TTKIQ5cS+HbbWYa2rsN9QT7mNsfoeDrZYmut42pimrlNqTCklOy9spcRa0fwyo5XsBJWfNnzSxbft5hOPp3MbV61Qjn6/FjZwNA50HYM7J6hze71enNbVWrcnWxZ9HQHXOytGT3/HyKvp5jbpGJJycjm5WVh1Hax553BgeY2xyTodAIfV3uuVpMZ/cHYg4zdNJZntzxLYnoi73d9n1WDVtHbv3eVCslVFqpnjL44dFbwwAwtY2fPV5BxCx76TvsSqET4ujmwaGxHHp29l5Hz9rPquS54W2g45IN1J7gYn8ovz3Sq0vo9Pq72RFfxGX3YtTC+DfuWv6P/xsvBizc6vMGjTR7F1qp6P4A2N2pGXxhCQJ//wr1vQ/hyWD4KsirfTKyRdw3mj+nAjeRMnpr3D0lpWeY26S62nohlyf5LPNO9AZ2quL67r5tDlQ3dRFyPYMKWCYxcP5LTCad5td2rrBu6jieaP6GcvAWgHH1RCAH3vAr3fQan1sKSYZCRbG6rSk2Inxvfj2zLubhkxi08QHpWjrlNus2N5AxeX3WUZrWdeaVfE3ObY3J8XR2IvZVBjr7yPfspipPxJ5n812QeW/sY4dfDeanNS6wfup5RgaNwsHYwt3mKXJSjL4kOz8CQ7yFyN/z0EKTGm9uiUtO9cU1mDA8h9GICk5YcIjvH/M8dpJS8sTqcm2nZfDkiBDvrqi/85eNmT45ecu1W5bs7LMiZhDNM2TaFR/94lIOxB5kUMokNQzcwNmgsjjaq9aWloWL0hhA8AmydYOXTsOABGPkrOFcuzZIHWvmSkJrF279F8PqqcD59pJVZdXFWhEax+Xgs0+5rTrPa1aOaMX/RVF66ZWXjfNJ5ZoXNYmPkRpxsnJgQPIEnWzypKlItHIMcvRBiAPAVYAXMlVJ+VGC9K/AzUC93n59JKefnrosEbgE5QHZRWgwWT/MH4fFlsPQJmD8Anvod3CpX9enITv7EJ2cyY8tpPGvY8uZ9zc1ix6Ubqfz3j2N0auDB2G71zWKDOfB1zXP06bStZEW/F29eZPaR2ay7sA47KzvGBY1jVOAoXO2qZ/FXZaNERy+EsAK+BfoCUcABIcQaKeXxfJtNBI5LKR8UQtQETgkhFkspM3PX95JSXje28RVOw3th5G+w+FGYNxCe+g28GpvbqlLxQu9GxKdkMGfneTycbHmuR8Xq4uj1kldXHkEntEreqqK2aQg+udWx0UmV54HsleQrzD4ymz/O/YGNzoZRLUYxuuVoPOw9zG2aohQYMqPvAJyVUp4HEEIsBQYD+R29BJyFliBbA4gH7tYNrQrU6wij/4Sfh8K8AVoYx8dy2tuVRJ4uTnxqFh+tP0nNGnY83LZuhR1/0b5I/rkQz8cPB1HXvXrFcl3sbahhZ83VxMoRo49LjWPEnyNIzUrlsWaPMTZoLF4OVbujWVXFkIexdYDL+X6Pyl2Wn5lAc+AqEA68KKXMe+IngU1CiINCiPHltNcy8GkFYzaAtb0Ws7+039wWlQqdTvD5o8F0buDJm7+Gczr2VoUcN/J6Ch9vOEWPJjUZ1q56Scjm4etmXylSLKWUTN87nbTsNJY/uJzXO7yunHwlxhBHX9i9dcH8sP5AGOALhAAzhRB5T2e6SinbAAOBiUKIewo9iBDjhRChQojQuLg4Q2w3L16N4OkNmirmTw/B2a3mtqhU2Frr+GpECDXsrHnhl8MmT7vU6yWvrTyKtZXgo4eDqm11pI+rQ6XQu1l9ZjW7ruxiStspNHSr3LLXlQYpId00kiWGOPooIP/0qy7azD0/Y4DVUuMscAFoBiClvJr78xrwK1oo6C6klHOklO2klO1q1qxZur/CXLj5ac7eo4EWylkyHC79bW6rDMbbxZ7PHg3mZMwtPlx3wqTHWrA3kn8i43n7gRaVNuPEGFSGGX3UrSg+OfAJHWt35LFmj5nbnOqBXg9rX9ae/ZmgXscQR38AaCyEqC+EsAVGAGsKbHMJ6A0ghKgFNAXOCyGchBDOucudgH5AhLGMtwhqeMOY9dBrGlz+B+b11z6s05sqhShar2bejO1Wn0X7LrLpWIxJjnHhegqfbDxJr6Y1ebQCnwdYIr6uDtxIybSowrX86KWet/a8hU7oeK/re+iEKrUxOfoc+GMyhM6Dxn21VG4jU+KnKKXMBiYBG4ETwHIp5TEhxHNCiOdyN3sP6CKECAe2Aq/nZtnUAnYLIY4A/wBrpZQbjP5XmBt7F+jxGkyJgAEfQ+IlWPIozO6mNTfJsezn0q8NaEqgrwuvrTpq9IwQLWRzBBsrHf8bWrU05suCT24ufYyFhm9+Ov4TB2MP8nqH1/GpUfVURC2OnGz47Xk4/DPc8xr0ecckPa0N+rqWUq6TUjaRUjaUUn6Qu2y2lHJ27vurUsp+UsogKWVLKeXPucvPSymDc1+BeWOrLLZO0Ok5eDEMHpoFOVmwaizMbAsHfrRYvRw7ayu+eaw1mdl6XloaZtQS/fl7IzkQmcD/PdCC2q6WKapWkfi6/tuAxNI4l3iOrw99TS+/XgxuONjc5lR9crLg1/Fa06Ne0+DeaSZx8qAkEEyDlQ2EPA7P/w0jloCjlxZ/+zJIkz820QOX8tCgZg3+OyiQ/Rfi+W7bWaPs88L1FD7deJJ7m3nzSDUP2eRxuzrWwmb0Wfospu6aipONE9M7T6/2d14mJztTq7SPWKUJKPZ4zaSHU47elOh00Ox+GLcFRv0JtVtqDU1mtIQt/4Xka+a28A4eaVuXQcG+fLn1DKGR5dP0ydFL/rPiCLZWOv43tPpm2RQk767G0uSKfzj6AyfiT/B/nf8PT4eqrSJqdrIzYMUoOLEG+v8Pur1k8kMqR18RCAH1u2vFVeN3aBW2u2doDv/PlyEh0twWAlox1QdDWuLrZs+LS8PKJWs8f88FQi8mMP3BQOO2BcxMhWO/WmwYrCTsbazwdLLlqgVVxx67fow5R+fwQIMH6OPfx9zmVG2y0jUZlVPrNGXczs9XyGGVo69ofENg2EKYfBCCh8OhRfBNWwhbYm7LAHC2t+HrEa2JvZnOm6vDkWXIHDofl8ynG0/Ru5k3Q9sUrK0rB2e3wHedYMVo+POlSpHVVBiaLr1lfFGlZ6czdfdUPB08mdpxqrnNqdpkpsIvI7Tr+MGvNGXcCqJKOfqzCWfJ0Vtm2tpdeDaEQd/AS0fBvyv8NgH2zjS3VQC0rufOK/2asjY8mmUHLpc8IB85esmrK45gZ63jQ2OFbJLjYNU4+PlhsLKF1k/CkV/gnx/Kv28z4ONqbzF6N18f/poLSRd4r+t7SoHSlGQkaz0tzm+Hwd9C29EVevgqI1OcmpXKUxuewtXWldGBoxncaDD21pUgy8PFF55YoTmyTdMg9Tr0nm6yp++G8uw9Ddhz9jrv/HGMtv7uNK7lbNC4ebsvcOhSIl8MCy5/yEZKOPwTbHobslKhxxvQ/WXQ2Wh9ATZO1Z57+Hcp33EqGF83B/aeu2FuMzgQc4Cfj//M8KbD6eJbuc5hpSL9piaEGPWP1pO61bAKN6HKzOjtrOx4t8u7uNu78/7+9+m/qj/fH/mepAzLy3C5C2s7eHSB9i2/ewb88YJWRGFGdDrBF8OCcbS1ZrKBEgnn4pL5bNMp+jT3ZkjrcoZs4k7DgvthzWTwbgHP7YZeU7VzpdPBkNngHgDLn4KbBQu1LRtfN3uSM7K5mW6+1o7Jmcm8tfst/Jz9eLnty2azo8qTlqhVzUcdgEfmmcXJQxVy9FY6K/r492HxfYuZ138eLTxbMDNsJn1X9uWTA58Qk2Kaqk+jobOCB76E7q9qcfsV5u9T6+1iz+e5Egn/K0EiIS/Lxt7Gig+HlCNkk50B2z+C2V0hNgIe/BpGr4WaTe/czt4Vhi+GrDRYNlIbV0nIk4CINjBOn63PZn/0fjJzMkve2EA+Df2UmNQYPuj2geoIZSpS4zUdrKth2nO5wCFmM6XKOPo8hBC0r92eWX1msfLBlfSu15slJ5YwcNVApu2expmEM+Y2sWiEgN5vaylXJ/6AxY9ot31mpFczb57uWp+F+y6y+Xhskdv9uPs8hy4l8t9BgXiXNWQTuUerJt7+P2g+CCaFQttR2gy+MLybwUPfwZVQWG/aPGRj4utWuqKprZe2Mm7TOO5bfR+LTywmPbt8E4Adl3ew+sxqxgSOIcQ7pFz7UhRByg1YNAhij8Hwn7XGRWakyjn6/DT1aMr/uv+PdUPXMaLZCDZf3MzQNUOZuHUioTGhZcooqRA6P6/1qb24FxY+qD2MNCOvD2xKCx8X/rPySKGl+2evJfPZptP0bVGLwSG+pT9AWoIWollwH2SnwxOr4JEfNR2hkmgxGLpNgYML4ODC0h/bDPxbNGWYo798S3sg7lvDl4/++YgBqwYwP2I+qVmppT52QnoC0/dOp4l7E54PqZjUvmpH8jVY+ABcPwOP/QJNB5jboqrt6PPwreHL6x1eZ9PDm5gYMpHwuHDGbBzDk+ufZOvFreil+Ztl30XwCO0iiTuptS5MvGQ2U+ysrfjm8dZkZOl5adnhOyQScvSS/6w8gqOtFR8MaVm6kI2UmhbQzPZweDF0eUGrJm5cylzue9/WahPWvQpRoaUbawa8ne2x0gmDQzcxKTG42LqwaOAi5vefTxP3Jnxx8Av6rerH90e+52amYXd9Ukre//t9kjKT+LDbh9ha2Zbnz1AUxs1o7dlSQiQ8vhwaWUZdQrVw9Hm42bvxXPBzbHxkI9M6TuNG2g1e2v4Sg38bzKrTq4waAzUKTfprrQtT4uDH/nDNtFLCxdGwZg3+OziQv8/HM2v7vxIJc3ed53BeyMa5FCGbhEgtXXLVWHD1g/Hbod97ZVPu01nBwz+Cc20tXm9hFccFsdIJajnbGRy6iU2NpZaT1oy+Xe12zOk3h5/v+5mQmiHMDJtJ/5X9+ebwNySkJxS7n/UX1rPpojbZaerRtNhtFcWQlgBXDmnyBTs/g98nwvz74YsW8EVzSLoCT6yEBj3MbelthCWGL9q1aydDQ00/M8vWZ7Pl4hbmRczjRPwJajvVZnLryTzQ4AHLkmeNidCe3GdnaBeQX3uzmCGl5MWlYawNj2b5s51wdbDhvq9307NJTb4f2dbw2fzxNbB6vOage/8ftB+nvS8v0Ufhx35Qp43WvN3Kpvz7NBGPzNqLtZVg6fjOJW477I9heDp4MqvPrLvWnbhxgh/Cf2Dzxc04WDswvOlwRgWOuqsbVGxKLEPWDKG+a30WDliIta7KZFYbH70ekmMg/gIkXLj7Z1qBL1Qnb/CoD+71tZ/NH4RagRVuthDioJSyXaHrqrOjz0NKyb7ofXx16CuO3zhOU/emvNzuZcvKLY6/AD8NgeRYGP6T2W4Jb6Zncf/Xu9DrwbOGLZfiU9k05R7DZ/Pnt2s5xT4h8Oh8cDWy2NnR5bD6Geg4AQZ+ZNx9G5HJvxzmaFQiO/7Tq8Rteyzrwb317mV65+lFbnM24SxzI+ay/sJ6bHQ2PNz4Yca0HENtp9pIKZmwdQKHYg+x4sEV+Lv4G/NPqTpk3NKK8PbNhNR8dQ7CSmsylOfI8/90DwC7GmYzOT/FOXr1tY6WqdPFtwudfDqxMXIjXx36imc3P0sX3y5MaTuFZh7NzG2idmE9vVELdywZoeWRBz1S4Wa45EokPDp7H1cS0/j6sdaGO/krhzSdD8/GWpGYg5vxDWw1TDvO/lng21qTmbBAfF3t2XgsHb1eotMVfSeUkZNBfHo8tRxrFbu/Ru6N+Kj7R0wInsCP4T+y/NRylp9ezkONHsLb0Zs9V/bwZsc3lZMvjDwHv/cbSIvXJlFNBmid4zzqa6FFC747NATl6POhEzoG1h9I73q9WXZqGd8f/Z5hfwzjwYYPMilkkvkbMTjXgjFr4ZfHtEratIQK1cvIo3U9dz55pBWR11N4sJWB5yTutJYu6ugBI1ebxsnn0e89iAmHP17UUjB9gk13rDLi6+ZAZraeGymZ1HS2K3K7a6na84aSHH0e/i7+vNv1XZ4Lfo55EfNYfWY1WfosOvl0YnhTy/zSMxsZyXDgB9jzda6D7ws934C6hU6KKzXK0ReCrZUtI1uMZHCjwcwNn8vi44vZcGEDT7R4gnFB48yrCWLvCk+u0rSs170KKdeh3dOgz9IaGeizIScz3/ss7Xd9ltbNRp/7e062tj6gq3b7WUqGtilFyCXpivaMQei0h8vOtUt9vFJhZaNVGs/pAcue1BRDHT1Me8xS4pMnV5yUVqyjzyv0q+1UunPmW8OXtzq9xfhW41l/YT33N7jfsp47mZNq5ODzUI6+GFxsXXi57cs81vQxZobNZEHEAlafWc34oPGMaDbCfOlpNg4w7Cct93zHR9qrrNg6a2Gg5g8Yz778pMZrTj49CUb/qYm5VQQ1amrnaP4A7UvxyVXGeeBrJG7n0iem06qY78zYVK1ILS/rprR4O3ozKnBUmcZWOTKS4cBc2Pu1FoNv1EfTTzJTckNFohy9AfjU8OGDbh8wssVIvgj9gk9DP2XJySW82OZF+gf0N89Mycpaqwpt3Fe7aK1stZmszkZbp7PJXZb33ibf+tyfWamars6yJ7R+lT2nFl2FWhYyUzTFvvgLWrimokModdvC/Z9rX4hb34W+/63Y4xfDv46++BTL2BTN0dd2NPFdUFWmGjv4PJSjLwXNPJoxp98c9l7ZyxcHv+C1na+x8NhCXmn3Cu1rm+GiEQJaDi3fPsZs0Noc7vwEoo9o6nrGiJ9nZ2o57VcOajPrgG7l32dZaPOU9nB2z5faw9nAh8xjRwHcHW2ws9aVKFcckxKDs61z5dSjSUuEzGTjZ1YZSmaK5uD3fKU5+Ia9tRCNXwfz2GNGVNCuDHSp04VlDyzjg24fcCP9Bk9vfJqXt79MVo751AjLjI29po9932dwbiv8cC9cO1m+fer1mr7+ua1agwVThYUMZeDHULc9/Pa8WYvO8iOE0BqQlNA7NjY11uAHsRbFub/gmzYwIxCWDIcLuyquUUxagubcv2wFm/9PS+Udu1m7q6yGTh6Uoy8zVjorBjUcxB8P/cHEkIlsvriZz0I/M7dZZUMILXtn1B+QcRPm9taKmsqClLDhdYhYCX3e0WbU5sbaTrursHXS0jvTEs1tEaCJm5UYuslXFVsp0Othxyfw01CtkKjby5pE78IH4Pt74Mgy7W7PFMc9vx1WjoXPmuY6+Fbw9KZq7eDzUI6+nNhb2/Nc8HOMbDGSJSeX8Me5P8xtUtnx76JlqNRsCstHwtb3Sq+Lv+MT+GcOdJ4EXV8yiZllwsUHhi2CxIvazN4CCgV9XB1K1LuJSYmpPPH51Hjtmcy2DyDoUXhmK/SZDlOOaXd22enw63j4Kljru1CwwrQsJF2BHZ/C1yGwaDCc3axNLp7dqfVortex/MeoAihHbySmtJ1C21pteXffu5yKP2Vuc8qOax0Ysx5aj4Rdn2m33Yb+Qx6YC9s/hODHoe97Zu+SdRf+nTW7Tq3VimPMjK+rPddupZOVU7ioXmZOplYsVRlm9FcOwfc9tFn1/Z9rz3rydItsHLSmOs/vh8dXgFdj2PKOpg2z7j8Qf750x8rOhOO/w8+PwJctYdv74O4PQ+fCK6fg/s8ssnbCnBjk6IUQA4QQp4QQZ4UQbxSy3lUI8YcQ4ogQ4pgQYoyhY6sKNjobPuvxGS62Lry07aXK0dmqKKzttH62D8zQ/nHn9ILY48WPiVgNa1/VKgoHfW3c7B1j0mmCpnW/5R24uM+spvi6OaCXEHuz8Fl9XrGURc/opYTQeTCvPyC16u324wr/ktfpoEk/GLVG6xjWYjCEzoev22ghtYv7ir/TunYSNk7ThMOWP6VpvXd/BV4I08KOrR7VvlQUd1Hif6MQwgr4FhgItAAeE0K0KLDZROC4lDIY6Al8LoSwNXBslcHLwYvPe35OTGoMU3dNtUz5Y0MRQivEGr1WS8Oc2weO/Vr4tuf+0kTK6nXSCpUsuVxcCBg8U5sBrhxjVq1/n9wUy+giHsjmFUtZ7Iw+M1V76P7nFAjoroX96rY1bGztIK1+46VwrQ9w5G6t5uGHezXp6pxsbbuMW1qfgbl94LuOsH+2dmf2+AqYEgH3vqXJFCiKxZBpVwfgrJTyvJQyE1gKDC6wjQSchSZfWAOIB7INHFulCPEO4fX2r7Pryi6+P/K9uc0pP/U6av/AtQJhxWhtJpw/bh91EJY+qcX1H1taOWZU9q5avD4tQZNJNlN/Xl/X4jtN5RVLWeSM/sY5zfkeWarVXzyxApw8S78fFx9NwfTl41rmV3qi9pl8HaIVun3WVKv1SL8J/d6Hl09qHZua9LOoAjhLxxBHXwe4nO/3qNxl+ZkJNAeuAuHAi1JKvYFjARBCjBdChAohQuPizNtRqbwMbzqcQQ0HMevILHZG7TS3OeXHxUeram07RnuItvgR7cFb3CntvZOXVnlqSv0aY1M7CO77FC7sgB0fm8UEn3zVsYVR3qpYk3F8DczpCbeuwpMrtdz08jpdWyct82vSQRjxC7jVgzNbtDqRsZth4n7oMlmreFaUGkMKpgp7olYwkNYfCAPuBRoCm4UQuwwcqy2Ucg4wBzSZYgPssliEELzd6W1OJ5zmjV1vsOz+Zfi5+JnbrPJhbQcPfgm+IVosfk5PbSass9ayG0ytX2MKWo/U4sI7PgG/jtCod4UevoadNS721kUWTcWkxOBs44yTTRmasZiCnCztjm7fTKjTFh5dqMn3GhOdDprdp70URsOQGX0UkP/TrIs2c8/PGGC11DgLXACaGTi2SmJvbc8XPb9AIJiyfQpp2YZ1E7J42o6GMeu0JigZN7WZfEXp1xgbIbQMEe8WmhpoUlSFm+Dr5lD0jD7FgnLob8XAwkGak28/TsvMMraTV5gMQxz9AaCxEKK+EMIWGAEUrKa5BPQGEELUApoC5w0cW2Xxc/bjo+4fcTrhNO/ue9dym5GXFr8O2q30xP1aUUplxtYRhi3UFD1XjNFmrRWI5uiLjtFbRFVs5G6Y3R2iw7QUxvs/1+7wFJWGEh29lDIbmARsBE4Ay6WUx4QQzwkhnsvd7D2gixAiHNgKvC6lvF7UWFP8IZZK97rdmRAygT/P/8kvJ38xtznGw8ENXHzNbYVx8GqspZNG/QObi+7iZAp8XO2LDd2UVp7YqGRnaj1RFw7SHmA/85eWwqiodBgkaialXAesK7Bsdr73V4F+ho6tbjzb6lmOXT/Gpwc+pYVnC0K8Q8xtklnRSz1xqXF4OXhhZSmZEy2HwqV98Pe3Wppoi0EVclhfNwcSUrNIy8zBwfbfc5GVk8WN9Bvmm9Gf3gQbp8KNsxA4RPsitHM2jy2KcqPUKysAndDxYfcPGfHnCF7e/jLLH1x+V/Pm6kLUrSje2vMWB2MPYm9lTwO3BjR2a0xjd+3VxL0JnvaehjcaNyb93tfUNn+fqKWTVsCzB1+33BTLpDQa1vy39+i1tNzOUhUdo487DRvf1KQEPBtp+epNCp3DKSoRytFXEC62LszoOYMn1z3JK9tfYW7/udjoLLiwyMhIKVl1ZhWfHvgUndAxKWQSSZlJnEk4w+4ru/n93O+3t3W3c6eRe6M7vgAauzU2vVSvtZ1W8DW7OywfBeM2m7wuwMc1t2gqMf0OR3+7s1RF5dCnJebqFH0PNo7Q/0No/wxYm6m5jsKoKEdfgTT1aMo7Xd7hjV1v8EXoF7ze4XVzm1QhxKXGMX3vdHZd2UVHn4681+W9u/rvxqfHczbhLGcSz3AmQXv9evbXO7KV6tSoQ2P3xgR5BTE6cLRpOny51dN0WpYMg/WvaSELE+Kb6+ivFojT5zUcMfmMXp8DhxbBX+9ptRFtR0Gvt1S+ehVDOfoK5v4G9xN+PZyfT/xMkFcQ9zWo2vnCGy5s4P3975ORncEbHd7gsWaPFdqRy8Pegw4+Hejg86+crF7quZJ85a4vgO2Xt+Ni68KIZiNMY3ST/pq87u4voF4XCHnMNMcBarnaIcTd1bG3i6VMGaOP3A3r34DYcPDvCgP+p8TAqijK0ZuBV9q9wokbJ3hn3zs0cm9EE/cm5jbJ6CSmJ/LB/g/YELmBVl6t+KDbBwS4BpRqHzqhw8/ZDz9nP3rV63V7+UO/PcSGyA2mc/QAvaZpOup/TtGcXy3TSDTZWVvhVcPuLrnimJQYatjUoIZtjSJGloOEi5pe+/HfwNVPC1e1eMjy1EYVRsNCJQarNnlKl042TkzZNoWbmTfNbZJR2Rm1kyFrhrDl4hYmt57MwoELS+3ki6N//f4cij10W93RJFhZw8M/gr2LppSYcctkh/J1tb87dGOKHPrMFPjrA/i2A5zeqH2ZTTqgZdUoJ1+lUY7eTNR0rMkXPb/gavJVpu2aZhalS73Uk63PNtr+UrJSeGfvO0zcOhE3OzeW3L+E8a3GY60z7o3jgIABSCSbIjcZdb934VxLc/bx52DNCyZrVlJY0ZRRq2KlhKMr4Jt2Wm/g5g/C5FDo8VrlEKFTlBvl6M1Ia+/WvNr+VbZHbeerQ19V6LGvpV5j8G+D6fJLF8ZsGMOMgzP469JfXE+7Xqb9HYw9yMNrHmb1mdWMaTmGZQ8so7lncyNbrVHftT5N3ZuyIXKDSfZ/58G6a1K4x1ZrjVVMgI+rA9FJ6XdUTsekGqlYKi0B5g+E1eOghremF//wXPM17FYUyYnom+w8bRpBRxWjNzOPN3uc84nnmRcxjzo16jCs6TCTHzM5M5nntzxPbGosgxoO4tj1Yyw6tohsqc3u69SoQyuvVgR7B9PKqxXNPJphU4TGfEZOBt8c+oZFxxdR17kuCwcupLV3a5P/DQPqD+CrQ18RnRx9VwaP0ek6BS7thw1ToU4bTdDLiPi62ZOamcPNtGxcHW20Yqk0IxRL6fVan4CoA/Dg15qIm6U2hKnm6PWSqavDiUpIZedrvXC0Na5rVo7ezAghmNpxKjGpMXyw/wNqO9Xmnrr3mOx4WfosXt7+MmcTzzKz90y61ekGQHp2OifjT3Ik7ghH4o5w6Noh1keuB8BWZ0tzz+YE1wymVc1WBNcMprZTbY7dOMa0XdM4l3SO4U2H83Lbl02f655Lf//+fHXoKzZGbmR0y9GmPZhOpzXJ+L4HLB8Nz+4ARw+j7d43V674SmIaro42xKXFIZHld/Q7PoYzmzSd97ajjGCpwlT8cuASYZcT+XJ4iNGdPChHbxFY66z59J5PGb1hNK/ueJWFAxaaJOwhpeSdve+wL3of73Z597aTB01tM8Q75A55hpiUGMKvh3Pk2hGOXj/KslPLWHR8EQDeDt7Ep8fjYe/B7D6z6Vqnq9HtLQ4/Fz9aeLZgQ+QG0zt60Bz7owu0lnm/T4QRS4z2ANMntwFJdFIaLXxd/i2WKk/o5tQG2PGR1r+3/ThjmKkwEXG3Mvh4/Um6NPRkcIhp9KOUo7cQHG0cmdl7Jk+se4KJWyey+L7FRg9JfBv2LWvOreH5kOcZ0nhIidvXdqpNbafa9PXvC2j6K6cTThMWF8bRuKO42LowqfUkXO1cjWqnoQwIGMAXB7/g8q3L+DlXgGRu3bbQ7z3Y8IYm19tlslF2mzejv5rbUrDcOfQ3zmkhm9qt4IEvVEaNhfPhuhOkZ+l576GWJpP+UAE7C8Lb0Zvven9HWnYaz299nluZxkvpW3l6Jd8f/Z6hjYfyXKvnSh5QCDZWNgR6BfJE8yf4+J6PmdZpmtmcPED/gP4AbIzcWHEH7ficlrWy5R0tbm8Eataww8ZK3M68KVdVbEYyLHtSCzcN/1ll1Vg4e89e59fDV3iuR4M7JDCMjXL0FkZj98bM6DWDyKRIpmyfQpYR9NF3Ru3k/b/fp1udbrzV6S3zCIaZAN8avrSq2apiHb0QMGimlrWycgyk3Cj3LnU6QS0Xe6JzHX1MagxONk4425ZSLVJKWDMZ4k7CI/O0BugKiyUjO4e3fovA39OR53s1MumxlKO3QDr5dOKdLu+wP3o//93333I1LDl2/Riv7niVJu5N+LzH51VOSG1AwABOxp8kMimy4g7q4Ka10UuJg1+f1bJbyomvq8O/oZuUMhZL7ftWSwPt/X/Q8N5y26QwLd/vOM/56ym8O7gl9jamletWjt5CGdxoMBOCJ/D7ud+ZfXR2yQMK4fKtyzy/9Xk87D34rs93FZYRU5H08++HQFRMTn1+fEM0hcezm2HPl+XfnZv9v6GbslTFXtipyRo0HwRdXyq3PQrTEnk9hZnbzvJAKx96NDG9gJxy9BbMhOAJDGo4iO/CvmPNudJ1YExIT2DClgnkyBxm9ZlVZfXvaznVorV364oN3+TRfhwEDoW/3ofIPeXalY+bA7E309HrZek7SyVFaW0QPRvCQ9+ph68WjpSSt3+PwM5Kx9sPmEZDqSDK0VswQgje6fwOHWt3ZPqe6eyPNuzhX1p2GpP/mkx0cjTf3PsN9V3rm9hS89I/oD9nE89yNuFsxR5YCHjwK3APgJVPQ3LZqxp9Xe3JypHE3Ezhetp1wx/EZqXDspFas/bhi1UXqErA2vBodp25ziv9mlDLxb5CjqkcvYVjY2XDF72+wN/FnynbppTozHL0Obyx8w2Oxh3lo3s+qpAqVXPTL6AfOqGr+PANaKJnwxZCeiKsfkbTdy8DeSmWx65Fla5Yav1/4OohGDILalY9FdSqxs30LN794zhBdVwZ2Tmgwo6rHH0lwMXWhe/6fIedtR3Pb32euNTCZ45SSj765yP+uvwXr3d4/Xb+e1XHy8GLdrXasTFyY7keXJeZ2kEw8GM4vw12fV6mXeR1mjpzI0rbpSGhm4MLtKYh3V/RUj4VFs8Xm04Tl5zBB0NaYqWruBCbcvSVBN8avszsPZPEjEQmbp1IalbqXdssOLaApaeWMjpwNE80f8IMVpqP/gH9ibwZyemE0+YxoM0oCBoG2z6E8ztKPTyvd2xk4hXAgGKpqFBY9x8tu6bXtFIfT1HxHI1KZNG+SJ7q5E+rum4Vemzl6CsRgZ6BfHrPp5xKOMVrO1+7Q2J43fl1fHHwCwYEDGBK2ylmtNI89PXvi5WwMk/4BrR4/QMzwKsxrBoHt2JLNdzVwQZHWyuu3NLkD4qN0Sdf0+LyzrU1GWWdaVPzysqO03H8efSquc2wCHL0kmm/RuBZw45X+jet8OMrR1/J6OHXg6kdprIjagcf/fMRUkr+if6HaXum0a5WOz7o9kGhrfqqOu727nT06ciGCxvME74BsKuh5ddn3IJVY0sVrxdC4ONqz/X0azhaO+JsU8RD1ZxsLcMmLV57+GpEcTVjsj48mqcXHOCFXw5zIDLe3OaYnZ//vkj4lST+74EWuNhXfC2LQR5BCDFACHFKCHFWCPFGIev/I4QIy31FCCFyhBAeuesihRDhuetCjf0HVEdGNBvB6MDRLDu1jI/++YiXtr2Ev7M/X/b60jQNsysJAwIGEJUcxfEbx81nRK0WcP/nELkLtn9UqqG+bg4kZcVRy6lW0dXLW6bDxd2a7LBPKyMYbHy2HI9l8i+HCfFzo667I1OWhXErvfwV3pWVazfT+WzjKbo39uKBViaW1C6CEh29EMIK+BYYCLQAHhNC3JH8KaX8VEoZIqUMAaYCO6SU+b/Ge+Wub2c806s3U9pOoa9/X5acXIK9tT2z+swyq+6MJXBvvXux1lmbL3yTR+snIOQJ2PkpnN1q8DBfVwdSc+KLjs+Hr9TE1Do8C8HDjWSscdl5Oo7nFx8i0NeF+WPaM2N4MFcT05i+5pi5TTMb7609QUaOnvcGm060rCQMmdF3AM5KKc9LKTOBpcDgYrZ/DPjFGMYpikYndHzY7UOeCXqGOX3nmL75RiXA1c6VLr5dzJd9k5/7PoOazbSUy5uGxal93OzJ0SXg7VCIo4+JgN8nQb3O0O99IxtrHPadu8Ezi0Jp6F2DhU93wMXehrb+Hkzq1YjVh65Uy3j9ztNx/HHkKhN7NiLAy8lsdhji6OsAl/P9HpW77C6EEI7AAGBVvsUS2CSEOCiEGF/UQYQQ44UQoUKI0Lg407TTqmrYW9vzQpsXaORuWkGkykT/gP5Ep0RzJO6IeQ2xddTy67PSYeVYLbZeArVdbBHWt3Cy8tQWZKVD7DE49pumSGnvqmniW1teeC40Mp6xCw9Qz8ORn8d2wM3xXxsn925MsJ8b036NILpAE/SqTHpWDm//HkEDLyee69nArLYY4ugLu9coarr0ILCnQNimq5SyDVroZ6IQotD2SVLKOVLKdlLKdjVrml77QVE16eXXCxudjXkkEQpSsyk8+CVc2gvbipiFSwk3o+H8DgJiFiOEpO7x3+HLIPigNszqAitGaZk2wxZpmTYWxpHLiYyZf4DaLvYsfqYjnjXs7lhvY6Xjy+EhZGbreXXFEfR6M99tVRDfbT/HxRupvPdQS+yszZsZZUjjkSggf1eHukBR92AjKBC2kVJezf15TQjxK1ooaGfpTVUoSsbZ1pludbqxKXIT/2n/H/NnILUaBpG7YfcM8GgAtjXgxlm4fhqun9GahOT2HbC3swXf2ninJ0Pd9lqc37ORlrLp2QhszXfrXxTHribx1Lx/cHOyYfEzHfF2Lrykv76XE//3YAumrg5n3p4LjOtu3hmuqTkXl8zs7ed4KMSXro3MrzNliKM/ADQWQtQHrqA588cLbiSEcAV6AE/mW+YE6KSUt3Lf9wPeNYbhCkVRDAgYwLbL2zh87TBtaxm3kXeZGPgxXDmkacXn4eqnOXC/jrcd+aWUq3D4I/5p/DED+vc2n70Gcjr2FiN//AcnWyuWjOt0u7q3KEa09+Ovk9f4ZMMpujbyormPSwVZWrFIKXn7twjsbHRMu79iRMtKokRHL6XMFkJMAjYCVsA8KeUxIcRzuevzNHSHAJuklCn5htcCfs190mwNLJFSmjklQlHV6enXE3srezZc2GAZjt7GAUatgYt7tWYgHg21GH4BEo4tBCA51fLlpM/HJfP4D/ux1gkWP9MJP4+SbRZC8NHQIPp/uYuXlobx+6SuJtFhT8/KYU3YVW5lZKPXS3KkJEcvkVKSo4ccKW8v1+u1dXnv9RL0UuJZw4667g7UdXfAz90RH1d7rK0Muzv8Pewqe8/d4P2HWlLT2a7kARWAQT1jpZTrgHUFls0u8PsCYEGBZeeB4HJZqFCUEkcbR7rX7c7mi5t5o8MbWFlC5aijBzR/oNhNYlNjEdKOuCQLsLcYLt1I5fEf9iOlZMn4TtQvRTaJZw07Pn20FWPmH+DTjaeMLtMbnZTGsz8d5GhUUrHb6QRY6QQ6IbDSCayEQKfT3gsgPjWT/IlbVjpBbRd7/DwcqOvuePsLoK67A34ejtRyscdKJ0hKzeL9tccJ8XPj8Q71jPq3lQfVHFxRJRkQMIDNFzcTGhtKR5+O5jbHIGJSYrAT7kQnpBttn1JKlodextfNgY71PbG1Lt8ziyuJaTw+92/Ss3P45ZlONPIuvSxyr6bePNXZnx93X6BXU2+6NTZODPvQpQSe/ekgqRnZzH6yDZ0beKHT3e3QhaDEfPbMbD3RSWlEJaQRlZDK5XjtZ1RCGrvOxBF7M+OO7a11Al83B6x1gviUTBaM6YCuAkXLSkI5ekWVpHvd7jhYO7AhckOlcfSxqbE4W3sRnWQ8R7//QjyvrwoHwNnOmnua1qRv81r0bFrzjhRIg+y7mc4TP/xNUmoWS57pVK4Y+9SBzdlz9jqvrAhj40v3lNqWgqw8GMWbq8Op7WrP4nEdaVKrfLr8ttY6/D2d8Pcs/G4lIzuHq4npd30JRCWk8p92zWhZx7KKF5WjV1RJHKwd6OnXky0Xt/BmxzcrRa/c2JRYPO0COZ+WRUpGNk525f/3XBcejb2NjhnDQthxOo4tJ66x9mg0VjpB+wB3+jSvRd8WtYp0aHlcT87g8R/+Ju5WBovGdiSobvkcmYOtFV+NaM2Q7/bw5q/hfPt4mzJVjWbn6Plo/Unm7r5Al4aefPt4G9ydTF9nYGdtRX0vp1KFrcyJcvSKKkv/gP6sv7Cef6L/oWudruY2p1iy9dnEpcXRvKZWFRudlFamsEh+cvSS9REx9GrqzcAgHwYG+aDXS45EJbLlRCxbjl/j/bUneH/tCRp716BPi1r0aV6LED+3O7TSE1IyeXLufq4kprFwTAfa+ruXy648WtZx5eW+Tfl4w0lWHbrCI23rlmp8UmoWk345xK4z1xndJYBp9zfHxsAHptUN5egVVZZudbpRw6YGGyM3Wryjv552Hb3UU8/VF4CrienldvQHLyYQdyuDgUH/ymPodILW9dxpXc+d//RvxqUbqZrTPxHLnJ3nmbX9HF41bLm3mTd9mtci2M+NcQtDOX89hXmj2tOxgWe5bCrI+HsasO3UNab/HkGHAA/qeRqWcXT2WjLPLAolKiGVj4YGMcKCHnxaIsrRK6osdlZ29PLrxZZLW3i709vYWFlu+CY2VdOvb+hRB8jkamL5pQLWhUdjZ62jdzPvIrep5+nI093q83S3+iSlZrH99DW2nLjG+ogYlodq3a5srATfj2xrtIem+bHSCb4YFszAL3fx8vIwlo7vVGIa47aT13jhl8PY2ehY8kwn2gdYplSzJaEcvaJKM6D+AP44/wf7ovdxT91C1TcsgpgUreFIUy8/hDjH1XI+kNXrJesjounZtKbBsX5XRxsGh9RhcEgdMrP1HIiMZ/upa3Rp5EWvpkV/WZSXuu6OvPdQS15aFsbsHeeYdG/jQreTUvL9zvN8vOEkLXxcmPNUO+q4FV+kpdBQjl5Rpens0xkXWxc2XNhg0Y4+NkWb0dd19qGW8xWiyzmjP3gpgdibGdwXVDZVU1trHV0beVVY+f5Drevw18lrfLnlDN0b1yTYz+2O9elZObyx6ii/hV3l/lY+fPZIMA62ll1vYEmoJxeKKo2NlQ296/Xmr8t/kZGTUfIAMxGbGou9lT0uti74uNlztZwqj2uPRmNrraN38xJ6z1oQ7w1uibezHS8tCyM181+1z5ikdIZ9v4/fwq7yar8mzHystXLypUQ5ekWVZ0DAAFKyUth9Zbe5TSmSmJQYajvVRgiBr6sD0YllD93o9ZINETH0aFKTGkZI0awoXB1t+GxYMJE3Unh/7QkADl9KYNDM3Zy7lsyckW2ZdG9jszXvqMxUnqtAoSgjHXw64G7nzsYLG+ldzzLFwmJTY293lvJ1s2fryViklGVyaocvJxBzM503gpoZ20yT06WhF+O7N+D7neex0Ql+OXCZ2i72/DS2I01rly8LqTqjZvSKKo+1zpo+/n3YHrWdtGzLbHwRmxpLLSfN0fu4OpCepSchtWx9VtcejckN25juAaopeblfE1r4uLBw30Xa+bvz+8SuysmXE+XoFdWC/gH9SctOY1fULnObchc5+hziUuPumNEDZUqxzMu2uadxTZztLTedtDjsrK2Y81Rb3n+oJQuf7lAhla5VHeXoFdWCdrXa4Wnvaf7G4YVwPe06OTKH2k5a96g8XfeyaN4cvpxIdFI697eyvE5UpaGuuyNPdvJXla5GQp1FRbXASmdFX/++7IraRWpWqrnNuYO8Yql/Z/Saoy/LjH59eDS2VpUr20ZhepSjV1QbBtQfQHpOOtsubzO3KXeQVyyVN6P3dLLF1kpX6hRLKTVtm+6NvXCppGEbhWlQjl5RbWjt3Zq6Neqy5OQSpLScBtUFZ/Q6naC2q32pUyzDLidyJTGtzEVSiqqLcvSKaoNO6BgVOIqjcUc5GHvQ3ObcJjYlFjsrO1zt/pX+9XWzL3XoZl14NDZWgj4tVNhGcSfK0SuqFQ81eggPew9+jPjR3KbcJib132KpPHxdHUr1MFZKybrwGLo18sLVQYVtFHeiHL2iWmFvbc8TzZ9g95XdnIo/ZW5zAG1Gnxe2ycPHzZ6Ym+nk6A0LMR2NSlJhG0WRKEevqHYMbzocR2tH5kXMM7cpwJ1VsXn4ujmQo5dcu2XYrD4vbNOvReVOq1SYBuXoFdUOVztXhjUdxsbIjUTdijKrLTn6HK6lXrudcZOHr2teimXJjl5KydrwaLo28sLVUYVtFHejHL2iWvJk8ycRQrDw2EKz2nEj/QY5MqfQ0A1oLQVLIvxKElEJKmyjKBrl6BXVklpOtRjUcBC/nv2VG2k3zGZHng59ns5NHqUpmlobHo21TtBPZdsoisAgRy+EGCCEOCWEOCuEeKOQ9f8RQoTlviKEEDlCCA9DxioU5mJ04GgyczJZcnKJ2WyISb2zWCoPF3sbathZlxi6kVKyPjyGLo28cHNUmjCKwinR0QshrIBvgYFAC+AxIUSL/NtIKT+VUoZIKUOAqcAOKWW8IWMVCnNR37U+vev15peTv5CSlWIWG27P6B3vno37uNqXGLo5dvUml+JTuT9IPYRVFI0hM/oOwFkp5XkpZSawFBhczPaPAb+UcaxCUaE83fJpbmXeYuXplWY5fmyqVizlZud21zpfN4cSZ/Rrw6Ox0qlsG0XxGOLo6wCX8/0elbvsLoQQjsAAYFUZxo4XQoQKIULj4uIMMEuhKD9BNYPoULsDi44tIjMns8KPH5MSQy3HWoU2GPF1K35GrxVJRdOloaeS8lUUiyGOvrAWN0VVcTwI7JFSxpd2rJRyjpSynZSyXc2aNQ0wS6EwDmNbjuVa2jXWnl9b4cfO33CkID6uDlxPziQ9K6fQ9ceu3uTijVSVbaMoEUMcfRTgl+/3usDVIrYdwb9hm9KOVSjMQmffzjT3aM68iHnopb5Cj11YVWweeZk3MUVIIayP0MI2/QNV2EZRPIY4+gNAYyFEfSGELZozX1NwIyGEK9AD+L20YxUKcyKE4OmWTxN5M5JtlypOwrioYqk8fF1zO00VEr7J07bp3MATDxW2UZRAiY5eSpkNTAI2AieA5VLKY0KI54QQz+XbdAiwSUqZUtJYY/4BCoUx6OPfBz9nP36M+LHCJIzj0+PJltlFzuh9cmf0hckVn4i+xYXrKSpsozAIa0M2klKuA9YVWDa7wO8LgAWGjFUoLA1rnTWjA0fz3t/vERobSvva7U1+zII69AXxyZvRF1I0tS48Gp2AfoGqSEpRMqoyVqHIZXCjwXjae1aYhHHBzlIFsbexwtPJlqsFYvR52TadGnjiVcPO5HYqKj/K0SsUudhZ2fFkiyfZc2UPJ+NPmvx4t2f0RWTdgKZ5UzDF8lTsLc6rsI2iFBgUurEEsrKyiIqKIj29dO3VFFULe3t76tati42NaVQahzUdxtzwucwLn8cnPT4xyTHyiE2JxVZni7ude5Hb+Lo6EHnjzqrddUe1sI3KtlEYSqVx9FFRUTg7OxMQEFBocYmi6iOl5MaNG0RFRVG/fn2THMPF1oVhTYex8NhCJt+ajJ+zX8mDykhMSgy1nAovlsrD182Bfef+FV3LkyTuUN+Dms4qbKMwjEoTuklPT8fT01M5+WqMEAJPT0+T39WNbD4SK2FlcgnjwhqOFMTH1Z5bGdncTM8C4HRsMufiUrhfhW0UpaDSOHpAOXlFhVwDNR1rMqjhIH47+xvX066b7DjFVcXm4VsgxXJteDRCQP+WKmyjMJxK5egVioritoTxCdNIGOulntjUWGo7Fu+wfd3uLJpaHx5NhwAPvJ3tTWKXomqiHL1CUQgBrgH08e/D0pNLSc5MNvr+49PjydZnlzij93H9d0Z/JvYWZ64lc38rFbZRlA7l6CuA7du388ADD5jbDKPzzjvv8NlnnwEwevRoVq40j9SvqRjbciy3skwjYVycDn1+vJ3tsNIJriam3Q7bDFDZNopSohx9JSQnp3A1Q1OPrW4EegXS0acji44bX8K4pGKpPKytdNRytuNqUhrrwqNp7++Bt4sK2yhKR6VJr8zPf/84xvGrN426zxa+Lkx/MLDI9ZGRkQwYMIBu3brx999/ExwczJgxY5g+fTrXrl1j8eLFALz00kukpaXh4ODA/Pnzadq06R37CQoKYteuXbi6uuLl5cWMGTN46qmnGDlyJKNGjaJRo0aMHDmSlBQtd3rmzJl06dKF7du389///hcfHx/CwsIIDw/njTfeYPv27WRkZDBx4kSeffbZQm0vzdhPPvmEn376CZ1Ox8CBA/noo4/44YcfmDNnDpmZmTRq1IiffvoJR0dHY5x2i2dsy7GM3zyeP8//ydDGQ42237wWgiXN6EHTvPn73A2uJqXzzoOqQZui9FRKR28uzp49y4oVK5gzZw7t27dnyZIl7N69mzVr1vDhhx+yaNEidu7cibW1NVu2bOHNN99k1apVd+yja9eu7NmzB39/fxo0aMCuXbt46qmn+Pvvv5k1axY6nY7Nmzdjb2/PmTNneOyxxwgNDQXgn3/+ISIigvr16zNnzhxcXV05cOAAGRkZdO3alX79+hWZX27I2JMnT/Lbb7+xf/9+HB0diY/X2goMHTqUZ555BoC33nqLH3/8kcmTJ5vwTFsOnXw60dyjOfMj5jO44WCsdFZG2W9saiw2Ohvc7YsulsrD182BgxcTEAIGqrRKRRmolI6+uJm3Kalfvz5BQUEABAYG0rt3b4QQBAUFERkZSVJSEqNGjeLMmTMIIcjKyrprH927d2fnzp34+/szYcIE5syZw5UrV/Dw8KBGjRokJSUxadIkwsLCsLKy4vTp07fHdujQ4bYj37RpE0ePHr0dF09KSuLMmTNFOnpDxm7ZsoUxY8bcnq17eHgAEBERwVtvvUViYiLJycn079/fGKezUiCEYGzQWF7d8Sp/Xf6Lvv59jbLfPB16nSg5eponV9zO351aKmyjKAMqRl8K7Oz+rUTU6XS3f9fpdGRnZ/P222/Tq1cvIiIi+OOPPwot7LnnnnvYtWsXu3btomfPntSsWZOVK1fSvXt3AGbMmEGtWrU4cuQIoaGhZGb+Gxt2cnK6/V5KyTfffENYWBhhYWFcuHCBfv36FWm7IWOllIXmqY8ePZqZM2cSHh7O9OnTq50MRZ96fajnXI954fOMJmGcVxVrCHkqlgNbqtm8omwoR29EkpKSqFNHa4m7YMGCQrfx8/Pj+vXrnDlzhgYNGtCtWzc+++yz244+KSkJHx8fdDodP/30U5EPT/v378+sWbNu3zWcPn36dly/JIoa269fP+bNm0dqairA7dDNrVu38PHxISsr6/aziOqElc6K0S1HE3Ejgn9i/jHKPg2pis2jfX0PmtV25oFg5egVZUM5eiPy2muvMXXqVLp27VpsdkvHjh1p0qQJoIVyrly5Qrdu3QB4/vnnWbhwIZ06deL06dN3zMTzM27cOFq0aEGbNm1o2bIlzz77LNnZ2QbZWdTYAQMGMGjQINq1a0dISMjt1Mn33nuPjh070rdvX5o1a1aaU1JlGNRwEF4OXvwYXn4JY73UF9tZqiCBvq5seOkeVSSlKDOiorrplIZ27drJvAeQeZw4cYLmzZubySKFJWGua2FexDxmHJzB0geWEuhZ9udE19Ou02t5L6Z2mMrjzR83ooWK6owQ4qCUsl1h69SMXqEwkGFNhuFs41zuWb0hOvQKhTGplFk3isIJDw9n5MiRdyyzs7Nj//79ZrKoalHDtgYjmo1gbvhcLiRdoL5r2aSS86piDQ3dKBTlRTn6KkRQUBBhYWHmNqNK80TzJ1h0fBHzI+bzbtd3y7SPvKpYQx/GKhTlRYVuFIpS4OngydDGQ/nj/B+3HXZpiU2NxVpnjYe9h5GtUygKRzl6haKUjA4cDZIyNybJS600pFhKoTAG6kpTKEqJbw1f7mtwH6vOrCIxPbHU42NSYlTYRlGhGOTohRADhBCnhBBnhRBvFLFNTyFEmBDimBBiR77lkUKI8Nx1oYWNVSgqG2MCx5CWncaSk6VvTBKbUnJnKYXCmJTo6IUQVsC3wECgBfCYEKJFgW3cgO+AQVLKQODRArvpJaUMKSrHszIQGRlJy5YtDd7eXPrspbVTUTYauTeil18vFp9YTGpWqsHjpJRaZymVcaOoQAzJuukAnJVSngcQQiwFBgPH823zOLBaSnkJQEp5zdiG3sH6NyAm3Lj7rB0EAz8y7j6rOTk5OVhZGUft0RIZFzSObZe3seL0CkYFjjJoTHx6PFn6LBW6UVQohoRu6gCX8/0elbssP00AdyHEdiHEQSHEU/nWSWBT7vLxRR1ECDFeCBEqhAiNi4sz1P4KJTs7m1GjRtGqVSseeeQRUlNTeffdd2nfvj0tW7Zk/PjxhYpeFbVNz549ef311+nQoQNNmjRh165dgOYgX331VYKCgmjVqhXffPMNAAcPHqRHjx60bduW/v37Ex0dfXt5cHAwnTt35ttvvy32b4iMjKR79+60adOGNm3asHfv3tvrPvnkE4KCgggODuaNN7QI3dmzZ+nTpw/BwcG0adOGc+fO3dUxa9KkSbe1fQICAnj33Xfp1q0bK1as4IcffqB9+/YEBwfz8MMP39bRiY2NZciQIQQHBxMcHMzevXt5++23+eqrr27vd9q0aXz99del+owqklY1W9GhdgcWHTO8MUlesVRJvWIVCqMipSz2hRaGmZvv95HANwW2mQn8DTgBXsAZoEnuOt/cn97AEeCeko7Ztm1bWZDjx4/ftawiuXDhggTk7t27pZRSjhkzRn766afyxo0bt7d58skn5Zo1a6SUUo4aNUquWLFCSimL3KZHjx7y5ZdfllJKuXbtWtm7d28ppZTfffedHDp0qMzKyro9PjMzU3bu3Fleu3ZNSinl0qVL5ZgxY6SUUgYFBcnt27dLKaV89dVXZWBgYJF/R0pKikxLS5NSSnn69GmZd67XrVsnO3fuLFNSUu6wuUOHDnL16tVSSinT0tJkSkqK3LZtm7z//vtv73PixIly/vz5Ukop/f395ccff3x73fXr12+/nzZtmvz666+llFIOGzZMzpgxQ0opZXZ2tkxMTJQXLlyQrVu3llJKmZOTIxs0aHDH+DzMfS3kZ0/UHtlyQUu56vQqg7b/6+JfsuWCljIiLsLElimqG0CoLMKnGhK6iQL88v1eF7hayDbXpZQpQIoQYicQDJyWUl7N/UK5JoT4FS0UtLM0X0aWgp+fH127dgXgySef5Ouvv6Z+/fp88sknpKamEh8fT2BgIA8++OAd47Zt21bkNkOHal2L2rZtS2RkJABbtmzhueeew9pa+3g8PDyIiIggIiKCvn01PfScnBx8fHxISkoiMTGRHj16ADBy5EjWr19f5N+QlZVVqN59YVr0t27d4sqVKwwZMgQAe3vDRLWGDx9++31RWvZ//fUXixYtAsDKygpXV1dcXV3x9PTk8OHDxMbG0rp1azw9PQ06prno7NuZ5h7NmRcxz6DGJLc7S6mHsYoKxBBHfwBoLISoD1wBRqDF5PPzOzBTCGEN2AIdgRlCCCdAJ6W8lfu+H1C2ckILoKBWuxCC559/ntDQUPz8/HjnnXfu0mpPT08vdps8TXsrK6vb6pOyEF14KSWBgYHs27fvjuWJiYmFasgXRX69e71ef9t5F3XMwrC2tkav19/xN+Ynv+Lm6NGj+e233wgODmbBggVs3769WPvGjRvHggULiImJ4emnnzb47zIXQgjGBY3jlR2vsOXSFvoHFN+UJTZFFUspKp4SY/RSymxgErAROAEsl1IeE0I8J4R4LnebE8AG4CjwD1qoJwKoBewWQhzJXb5WSrnBNH+K6bl06dJtR/vLL7/clhb28vIiOTm50CybPCdY3DYF6devH7Nnz77t+OPj42natClxcXG3j5+VlcWxY8dwc3PD1dWV3bt3A5SoF1+U3n1hWvQuLi7UrVuX3377DYCMjAxSU1Px9/fn+PHjZGRkkJSUxNatW4s8XlFa9r1792bWrFmAdndy86bWA3jIkCFs2LCBAwcOVJpOVr3r9SbAJYAfw38ssTGJKpZSmAODrjYp5TopZRMpZUMp5Qe5y2ZLKWfn2+ZTKWULKWVLKeWXucvOSymDc1+BeWMrK82bN2fhwoW0atWK+Ph4JkyYwDPPPENQUBAPPfQQ7du3v2uMm5tbidsUZNy4cdSrV49WrVoRHBzMkiVLsLW1ZeXKlbz++usEBwcTEhJy+0Hq/PnzmThxIp07d8bBwaHYfReld1+UFv1PP/3E119/TatWrejSpQsxMTH4+fkxbNgwWrVqxRNPPEHr1q2LPF5RWvZfffUV27ZtIygoiLZt23Ls2DEAbG1t6dWrF8OGDas0GTtWOiuebvk0J+JPsPfq3mK3VcVSCnOg9OgVFoVer6dNmzasWLGCxo0bF7qNJV4LWTlZDFg9AH8Xf+b1n1fkdvetvo+Wni35pMcnFWidojqg9OgVlYLjx4/TqFEjevfuXaSTt1RsrGwY1WIUB2IOcCTuSKHbSCmJTVHFUoqKRzn6KsrGjRsJCQm545WXPWOptGjRgvPnz/P555+b25Qy8UiTR3C1c2Vu+NxC1ydkJJCpz1QZN4oKR+nRV1H69+9faR5mVhUcbRx5otkTfHfkO84knKGx+513JXkNR1SMXlHRqBm9QmFEHmv2GA7WDsyLuDtOf7sqVoVuFBWMcvQKhRFxs3fjkSaPsP7Ceq4kX7ljneospTAXytErFEbmqRZPIYRgQcSCO5bHpsZiLVSxlKLiUY5eoTAytZ1qM6jhIH49+yvX067fXh6bEou3o3eJMgkKhbFRjt5AjKXzHhoaygsvvGAEi0xPjRo1AKVxXxbGBI4hMyeTxSf+rQaOSY1RGTcKs1Aps24+/udjTsafNOo+m3k04/UOrxt1n4XRrl072rUzXf+V7Ozs22JoCvMR4BpAH/8+LD25lKdbPo2zrTOxKbG08GxR8mCFwsioGX0pKEyPviiN+KK05vNrucfFxdG3b1/atGnDs88+i7+/P9evXycyMpLmzZvzzDPPEBgYSL9+/UhLSyvSrp49e/Lmm2/So0cPvvrqqyJtKkxbPjk5md69e9OmTRuCgoL4/fffTXwWqw/jgsaRnJXM8lPLVWcphXkpSr/YnK/Kokf/ySefFKkRX5TWfH4t94kTJ8oPP/xQSinl+vXrJSDj4uLkhQsXpJWVlTx8+LCUUspHH31U/vTTT0Xa1qNHDzlhwgQppSxWt74wbfmsrCyZlJQkpZQyLi5ONmzYUOr1eimllE5OTrf/9uI07isac18LpWH8pvGyx9IeMjo5WrZc0FL+dKzoz1GhKA+UU49ekUtBPfoPP/ywUI34PArTms/P7t27+fXXXwFNVMzd3f32uvr16xMSElLs+PzkacCfOnWqUJuK0pbPysrizTffZOfOneh0Oq5cuUJsbCy1a6uZpzEYFzSOpzc+zfdHvweUDr3CPChHXwoK6rU7OzsXqhGfR2Fa8/mRxQjK5Y3NG19c6Ab+1YCXRejW58kAF2Tx4sXExcVx8OBBbGxsCAgIuEtfXlF22tVqRyuvVqw6vQpQLQQV5kHF6EtBQT36Tp06FaoRbyjdunVj+fLlAGzatImEhIRy21iUbn1R2vJJSUl4e3tjY2PDtm3buHjxYrltUPyLEIKxQWORaF/qakavMAfK0ZeCgnr0kydPLlIj3hCmT5/Opk2baNOmDevXr8fHxwdnZ+dy2Vicbn1h2vJPPPEEoaGhtGvXjsWLF9+hGa8wDj39etLQtSFWwgpPe8tujaiomig9ejOSkZGBlZUV1tbW7Nu3jwkTJhAWFmZusyyeyngtHL52mKNxRxkVOMrcpiiqKMXp0asYvRm5dOkSw4YNQ6/XY2tryw8//GBukxQmorV3a1p7F92JS6EwJcrRm5HGjRtz+PBhg7efOHEie/bsuWPZiy++yJgxY4xtmkKhqEJUKkcvpbwr86U68e2335rbBLNjiaFGhcLSqTQPY+3t7blx44b6R6/GSCm5cePG7RoAhUJhGJVmRl+3bl2ioqKIi4sztykKM2Jvb0/dunXNbYZCUamoNI7exsaG+vXrm9sMhUKhqHRUmtCNQqFQKMqGcvQKhUJRxVGOXqFQKKo4FlkZK4SIA8oquuIFXC9xq4pH2VU6lF2lQ9lVOqqiXf5SypqFrbBIR18ehBChRZUBmxNlV+lQdpUOZVfpqG52qdCNQqFQVHGUo1coFIoqTlV09HPMbUARKLtKh7KrdCi7Ske1sqvKxegVCoVCcSdVcUavUCgUinwoR69QKBRVnErr6IUQkUKIcCFEmBAitJD1QgjxtRDirBDiqBCiTQXY1DTXnrzXTSHESwW26SmESMq3zf+ZyJZ5QohrQoiIfMs8hBCbhRBncn+6FzF2gBDiVO65e6MC7PpUCHEy93P6VQjhVsTYYj9zE9j1jhDiSr7P6r4ixlb0+VqWz6ZIIURYEWNNeb78hBDbhBAnhBDHhBAv5i436zVWjF1mvcaKsatirjEpZaV8AZGAVzHr7wPWAwLoBOyvYPusgBi0Iob8y3sCf1bA8e8B2gAR+ZZ9AryR+/4N4OMi7D4HNABsgSNACxPb1Q+wzn3/cWF2GfKZm8Cud4BXDficK/R8FVj/OfB/ZjhfPkCb3PfOwGmghbmvsWLsMus1VoxdFXKNVdoZvQEMBhZJjb8BNyGETwUevzdwTkpZ1grfciGl3AnEF1g8GFiY+34h8FAhQzsAZ6WU56WUmcDS3HEms0tKuUlKmZ37699AhesQF3G+DKHCz1ceQuvCMwz4xVjHMxQpZbSU8lDu+1vACaAOZr7GirLL3NdYMefLEMp9viqzo5fAJiHEQSHE+ELW1wEu5/s9CsNPrDEYQdH/gJ2FEEeEEOuFEIEVaFMtKWU0aBce4F3INuY+b0+j3YkVRkmfuSmYlHu7P6+IMIQ5z1d3IFZKeaaI9RVyvoQQAUBrYD8WdI0VsCs/Zr3GCrHL5NdYZXb0XaWUbYCBwEQhxD0F1hfWc7BCckmFELbAIGBFIasPoYVzgoFvgN8qwqZSYM7zNg3IBhYXsUlJn7mxmQU0BEKAaLQwSUHMdr6Axyh+Nm/y8yWEqAGsAl6SUt40dFghy4x6zoqyy9zXWCF2Vcg1VmkdvZTyau7Pa8CvaLc3+YkC/PL9Xhe4WjHWMRA4JKWMLbhCSnlTSpmc+34dYCOE8Kogu2Lzwle5P68Vso1ZzpsQYhTwAPCEzA1MFsSAz9yoSCljpZQ5Uko98EMRxzPX+bIGhgLLitrG1OdLCGGD5rQWSylX5y42+zVWhF1mv8YKs6uirrFK6eiFEE5CCOe892gPWiIKbLYGeEpodAKS8m4pK4AiZ1pCiNq5sVWEEB3QPoMbFWTXGmBU7vtRwO+FbHMAaCyEqJ97ZzIid5zJEEIMAF4HBkkpU4vYxpDP3Nh25X+mM6SI41X4+cqlD3BSShlV2EpTn6/ca/hH4ISU8ot8q8x6jRVll7mvsWLsqphrzNhPlyvihfb0+Uju6xgwLXf5c8Bzue8F8C3a0+pwoF0F2eaI5rhd8y3Lb9ekXJuPoD0U6mIiO35BuxXMQpsRjAU8ga3AmdyfHrnb+gLr8o29Dy0r4FzeuTWxXWfRYpBhua/ZBe0q6jM3sV0/5V47R3P/sXws4XzlLl+Qd03l27Yiz1c3tPDB0Xyf233mvsaKscus11gxdlXINaYkEBQKhaKKUylDNwqFQqEwHOXoFQqFooqjHL1CoVBUcZSjVygUiiqOcvQKhUJRxVGOXqFQKKo4ytErFApFFef/AS6mms7OqLfTAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", + "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", + "plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", + "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", + "# plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df['thres'],df['balanced_accuracy'],label='balanced_accuracy')\n", + "plt.plot(df['thres'],df['malware_precision'],label='malware_precision')\n", + "\n", + "plt.plot(df['thres'],df['malware_recall'],label='malware_recall')\n", + "\n", + "# plt.plot(df['thres'],df['benign_recall'],label='benign_recall')\n", + "# plt.plot(df['thres'],df['benign_precision'],label='benign_precision')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------thres= 5\n", + "0.7807339449541284\n", + "0.7803983228511531\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8771796676021932 recall_benign=0.8329355608591885 recall_malware=0.9214237743451981\n", + "------thres= 6\n", + "0.7680209698558322\n", + "0.7672955974842768\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8690278393147246 recall_benign=0.8220720720720721 recall_malware=0.9159836065573771\n", + "------thres= 7\n", + "0.7522935779816514\n", + "0.75\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8469601677148847 recall_benign=0.8092243186582809 recall_malware=0.8846960167714885\n", + "------thres= 8\n", + "0.7410222804718217\n", + "0.7363731656184487\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8359253730287315 recall_benign=0.7793240556660039 recall_malware=0.892526690391459\n", + "------thres= 9\n", + "0.7229357798165138\n", + "0.7206498951781971\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8143544260617431 recall_benign=0.7523452157598499 recall_malware=0.8763636363636363\n", + "------thres= 10\n", + "0.6933158584534731\n", + "0.6918238993710691\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.814834570191713 recall_benign=0.7789115646258503 recall_malware=0.8507575757575757\n", + "------thres= 11\n", + "0.6504587155963303\n", + "0.6525157232704403\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7961147758454633 recall_benign=0.7737556561085973 recall_malware=0.8184738955823293\n", + "------thres= 12\n", + "0.5994757536041939\n", + "0.6084905660377359\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8217002376430789 recall_benign=0.7898259705488622 recall_malware=0.8535745047372955\n", + "------thres= 13\n", + "0.5566186107470511\n", + "0.5670859538784067\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8364800633747029 recall_benign=0.7857142857142857 recall_malware=0.8872458410351202\n", + "------thres= 14\n", + "0.5104849279161205\n", + "0.509958071278826\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8069013085940719 recall_benign=0.7165775401069518 recall_malware=0.8972250770811921\n", + "------thres= 15\n", + "0.4596330275229358\n", + "0.45020964360587\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.776131378517819 recall_benign=0.6663489037178265 recall_malware=0.8859138533178114\n", + "------thres= 16\n", + "0.36094364351245084\n", + "0.3438155136268344\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7450445141432245 recall_benign=0.6166134185303515 recall_malware=0.8734756097560976\n", + "------thres= 17\n", + "0.27706422018348625\n", + "0.2688679245283019\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7486449097654531 recall_benign=0.828673835125448 recall_malware=0.6686159844054581\n", + "------thres= 18\n", + "0.2182175622542595\n", + "0.2112159329140461\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7783649209005548 recall_benign=0.8172757475083057 recall_malware=0.739454094292804\n", + "------thres= 19\n", + "0.1817824377457405\n", + "0.17033542976939203\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7919276932795569 recall_benign=0.8515476942514214 recall_malware=0.7323076923076923\n", + "------thres= 20\n", + "0.15176933158584535\n", + "0.139937106918239\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8100260871351396 recall_benign=0.8634978671541743 recall_malware=0.7565543071161048\n", + "------thres= 21\n", + "0.13171690694626476\n", + "0.11740041928721175\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.7995525110281643 recall_benign=0.8758907363420427 recall_malware=0.7232142857142857\n", + "------thres= 22\n", + "0.11507208387942333\n", + "0.10377358490566038\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8086390217969166 recall_benign=0.8748538011695907 recall_malware=0.7424242424242424\n", + "------thres= 23\n", + "0.0981651376146789\n", + "0.08962264150943396\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.820395115595552 recall_benign=0.8981001727115717 recall_malware=0.7426900584795322\n", + "------thres= 24\n", + "0.08256880733944955\n", + "0.07651991614255765\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8236087571719559 recall_benign=0.9074914869466515 recall_malware=0.7397260273972602\n", + "------thres= 25\n", + "0.06828309305373526\n", + "0.0660377358490566\n", + "{'learning_rate': 0.11303958411260817, 'max_depth': 17, 'n_estimators': 65, 'reg_alpha': 0.3079653289036747}\n", + "balanced acc=0.8465207631874299 recall_benign=0.9152637485970819 recall_malware=0.7777777777777778\n" + ] + } + ], + "source": [ + "import collections\n", + "pd_metric=collections.defaultdict(list)\n", + "for thres in list(range(5,26,1)):\n", + " print('------thres=',thres)\n", + " score,recall1,recall2,precision1,precision2=compute_metric_thres(X_train.drop('proportion',axis=1),X_test.drop('proportion',axis=1),thres,['malwareNum','label'] ,2)\n", + " pd_metric['thres'].append(thres)\n", + " pd_metric['balanced_accuracy'].append(score)\n", + " pd_metric['benign_recall'].append(recall1)\n", + " pd_metric['malware_recall'].append(recall2)\n", + " pd_metric['benign_precision'].append(precision1)\n", + " pd_metric['malware_precision'].append(precision2)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "pd.DataFrame(pd_metric).to_csv('../../xgboost_threshold_count.csv',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.DataFrame(pd_metric)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -2998,290 +3438,85 @@ " \n", " \n", " \n", - " thres\n", - " balanced_accuracy\n", - " benign_recall\n", - " malware_recall\n", - " benign_precision\n", - " malware_precision\n", + " malwareNum\n", " \n", " \n", " \n", " \n", - " 0\n", - " 0.01\n", - " 0.854852\n", - " 0.803783\n", - " 0.905921\n", - " 0.666667\n", - " 0.951744\n", + " count\n", + " 9538.000000\n", " \n", " \n", - " 1\n", - " 0.03\n", - " 0.859124\n", - " 0.810934\n", - " 0.907314\n", - " 0.681992\n", - " 0.951405\n", + " mean\n", + " 12.433424\n", " \n", " \n", - " 2\n", - " 0.05\n", - " 0.859041\n", - " 0.811287\n", - " 0.906795\n", - " 0.747967\n", - " 0.933746\n", - " \n", - " \n", - " 3\n", - " 0.07\n", - " 0.861583\n", - " 0.816000\n", - " 0.907165\n", - " 0.773900\n", - " 0.926798\n", - " \n", - " \n", - " 4\n", - " 0.09\n", - " 0.859165\n", - " 0.806697\n", - " 0.911634\n", - " 0.792227\n", - " 0.918642\n", - " \n", - " \n", - " 5\n", - " 0.11\n", - " 0.856246\n", - " 0.802343\n", - " 0.910149\n", - " 0.797671\n", - " 0.912508\n", - " \n", - " \n", - " 6\n", - " 0.13\n", - " 0.854162\n", - " 0.793584\n", - " 0.914739\n", - " 0.815186\n", - " 0.903394\n", - " \n", - " \n", - " 7\n", - " 0.15\n", - " 0.854121\n", - " 0.779817\n", - " 0.928425\n", - " 0.850000\n", - " 0.890196\n", - " \n", - " \n", - " 8\n", - " 0.17\n", - " 0.823251\n", - " 0.744875\n", - " 0.901627\n", - " 0.831004\n", - " 0.844768\n", - " \n", - " \n", - " 9\n", - " 0.19\n", - " 0.843597\n", - " 0.820850\n", - " 0.866345\n", - " 0.830092\n", - " 0.858739\n", - " \n", - " \n", - " 10\n", - " 0.21\n", - " 0.845145\n", - " 0.801878\n", - " 0.888412\n", - " 0.867886\n", - " 0.830658\n", - " \n", - " \n", - " 11\n", - " 0.23\n", - " 0.842389\n", - " 0.775773\n", - " 0.909006\n", - " 0.903000\n", - " 0.787805\n", - " \n", - " \n", - " 12\n", - " 0.25\n", - " 0.825224\n", - " 0.731474\n", - " 0.918974\n", - " 0.920762\n", - " 0.726683\n", - " \n", - " \n", - " 13\n", - " 0.27\n", - " 0.792966\n", - " 0.656809\n", - " 0.929124\n", - " 0.945545\n", - " 0.590984\n", - " \n", - " \n", - " 14\n", - " 0.29\n", - " 0.780516\n", - " 0.822699\n", - " 0.738333\n", - " 0.895194\n", - " 0.605191\n", + " std\n", + " 8.518853\n", " \n", " \n", - " 15\n", - " 0.31\n", - " 0.814713\n", - " 0.829871\n", - " 0.799555\n", - " 0.942602\n", - " 0.542296\n", + " min\n", + " 0.000000\n", " \n", " \n", - " 16\n", - " 0.33\n", - " 0.828884\n", - " 0.866559\n", - " 0.791209\n", - " 0.955109\n", - " 0.536313\n", + " 25%\n", + " 7.000000\n", " \n", " \n", - " 17\n", - " 0.35\n", - " 0.839876\n", - " 0.873832\n", - " 0.805921\n", - " 0.966131\n", - " 0.502049\n", + " 50%\n", + " 14.000000\n", " \n", " \n", - " 18\n", - " 0.37\n", - " 0.845233\n", - " 0.881143\n", - " 0.809322\n", - " 0.975028\n", - " 0.446262\n", + " 75%\n", + " 17.000000\n", " \n", " \n", - " 19\n", - " 0.39\n", - " 0.847486\n", - " 0.890740\n", - " 0.804233\n", - " 0.980054\n", - " 0.405333\n", + " max\n", + " 43.000000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " thres balanced_accuracy benign_recall malware_recall benign_precision \\\n", - "0 0.01 0.854852 0.803783 0.905921 0.666667 \n", - "1 0.03 0.859124 0.810934 0.907314 0.681992 \n", - "2 0.05 0.859041 0.811287 0.906795 0.747967 \n", - "3 0.07 0.861583 0.816000 0.907165 0.773900 \n", - "4 0.09 0.859165 0.806697 0.911634 0.792227 \n", - "5 0.11 0.856246 0.802343 0.910149 0.797671 \n", - "6 0.13 0.854162 0.793584 0.914739 0.815186 \n", - "7 0.15 0.854121 0.779817 0.928425 0.850000 \n", - "8 0.17 0.823251 0.744875 0.901627 0.831004 \n", - "9 0.19 0.843597 0.820850 0.866345 0.830092 \n", - "10 0.21 0.845145 0.801878 0.888412 0.867886 \n", - "11 0.23 0.842389 0.775773 0.909006 0.903000 \n", - "12 0.25 0.825224 0.731474 0.918974 0.920762 \n", - "13 0.27 0.792966 0.656809 0.929124 0.945545 \n", - "14 0.29 0.780516 0.822699 0.738333 0.895194 \n", - "15 0.31 0.814713 0.829871 0.799555 0.942602 \n", - "16 0.33 0.828884 0.866559 0.791209 0.955109 \n", - "17 0.35 0.839876 0.873832 0.805921 0.966131 \n", - "18 0.37 0.845233 0.881143 0.809322 0.975028 \n", - "19 0.39 0.847486 0.890740 0.804233 0.980054 \n", - "\n", - " malware_precision \n", - "0 0.951744 \n", - "1 0.951405 \n", - "2 0.933746 \n", - "3 0.926798 \n", - "4 0.918642 \n", - "5 0.912508 \n", - "6 0.903394 \n", - "7 0.890196 \n", - "8 0.844768 \n", - "9 0.858739 \n", - "10 0.830658 \n", - "11 0.787805 \n", - "12 0.726683 \n", - "13 0.590984 \n", - "14 0.605191 \n", - "15 0.542296 \n", - "16 0.536313 \n", - "17 0.502049 \n", - "18 0.446262 \n", - "19 0.405333 " + " malwareNum\n", + "count 9538.000000\n", + "mean 12.433424\n", + "std 8.518853\n", + "min 0.000000\n", + "25% 7.000000\n", + "50% 14.000000\n", + "75% 17.000000\n", + "max 43.000000" ] }, - "execution_count": 88, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.DataFrame(pd_metric)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(pd_metric).to_csv('../../xgboost_threshold.csv',index=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "df=pd.DataFrame(pd_metric)" + "X[['malwareNum']].describe()" ] }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 96, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3303,22 +3538,22 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 95, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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