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Phishing Detection Using NLP Project

Overview

This project aims to develop a phishing detection system utilizing Natural Language Processing (NLP) techniques. The goal is to identify potentially malicious content within emails and messages, providing an additional layer of security for users.

Technologies Used

  • Python
  • Natural Language Processing (NLP) libraries (e.g., NLTK, spaCy)
  • Machine Learning algorithms (e.g., SVM, Random Forest)
  • PowerBI for visualization and presentation

Key Features

  • Utilizes NLP to analyze text content for phishing indicators.
  • Trains a machine learning model to classify messages as either phishing or legitimate.
  • Implements a user-friendly interface for easy interaction.

Dataset

The model was trained on a diverse dataset comprising of both phishing and legitimate messages. The dataset was carefully curated to ensure a representative sample.

Results

The model achieved an accuracy of [insert accuracy here] on the test dataset, demonstrating its effectiveness in identifying phishing attempts.

Future Improvements

  • Incorporate more advanced NLP techniques for improved feature extraction.
  • Expand the dataset to enhance model robustness.
  • Implement real-time monitoring for immediate threat detection.
  • Zero Day Phishing in which whenever a phishing mail will created the ai will automatically block those spams.