A Hybrid Machine Learning and Reinforcement Learning Approach for Adaptive Phishing Email Detection

Phishing emails are the fake or fraudulent emails that are designed to trick people into giving personal information which may include passwords or credit card details or any other sensitive data .The goal of our project is to create a system that can automatically identify these fake emails and filter them out, and making email communication more safer .The system will analyze the content and patterns in emails to differentiate between the legitimate and the fraudulent emails. This project aims to develop a robust, automated system for identifying and filtering phishing emails, enhancing the security of email communication. Our approach combines machine learning techniques, specifically Random Forest classification, and reinforcement learning via Deep Q-Networks (DQN), to analyze the metadata, textual content, and embedded URLs of emails, discerning legitimate messages from phishing attempts. Through a feedback driven mechanism, the model can learn from incorrect predictions and adapt over time, providing an evolving and proactive defense against phishing threats. This system holds significant potential for deployment in both corporate and personal email environments, contributing to safer email practices across diverse use cases.