Reinforcement learning-based phishing detection model

Phishing attacks are a persistent cybersecurity threat, exploiting human vulnerabilities via deceptive emails and malicious URLs. This project introduces a novel Reinforcement Learning (RL)-based system to automate phishing detection and response. By employing advanced RL algorithms, such as Deep Q-Learning and Policy Gradient methods, the system dynamically learns to identify phishing indicators within email content and URLs through Natural Language Processing (NLP) and feature extraction techniques. The RL agent continuously adapts its detection strategies based on evolving threats and user feedback, aiming to minimize false positives while accurately identifying malicious activities. Upon detecting potential threats, the system initiates automated responses, including alert notifications, URL blocking, and user warnings, thereby enhancing security measures. Implementing this RL-based solution within Security Operations Centers (SOCs) or email security platforms offers a scalable, real-time defense against phishing attacks. This approach effectively safeguards sensitive information and strengthens organizational resilience against cyber threats.