IoT-Network-Monitor-and-Malware-Detector

Overview

This project is a network monitoring system that detects anomalies and malicious activities in a network. It uses a combination of socket programming, nmap, and machine learning algorithms to scan the network, identify devices, and detect potential threats.

Features

• Network scanning and device identification
• Anomaly detection using machine learning algorithms
• Real-time monitoring and alert system
• Integration with MongoDB for data storage and retrieval
• Streamlit-based dashboard for visualization and interaction

Usage

To start the scan, run

python Network_Scan.py

This will store the results in a MongoDB database. Now run

streamlit run streamlitz.py

To add anomaly to the network download this and start flooding packets to an intended IP address in the network.

To detect the anomaly run

python Detect_Mal.py

Note

The system performing the scan must be on the same network to effectively monitor activity. If the .pkl and .csv files are located in different directories, ensure that they are correctly added to the path.