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.
- Python
- Natural Language Processing (NLP) libraries (e.g., NLTK, spaCy)
- Machine Learning algorithms (e.g., SVM, Random Forest)
- PowerBI for visualization and presentation
- 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.
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.
The model achieved an accuracy of [insert accuracy here] on the test dataset, demonstrating its effectiveness in identifying phishing attempts.
- 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.