This research presents a comprehensive AI-based phishing detection system that leverages machine learning and deep learning algorithms to identify phishing emails and URLs with high precision. Phishing attacks remain the leading cyber threat globally, targeting individuals and organizations. Traditional rule-based approaches have proven inadequate against evolving tactics. Our system employs ensemble learning combining Random Forest, Gradient Boosting, SVM, and LSTM networks with 150+ engineered features from email headers, content, URLs, and authentication mechanisms. The approach achieves 96.3% detection accuracy with only 2.1% false positive rate, processing 10,000+ emails daily efficiently. Real-world deployment in a 5,000-user organization prevented 12,500 weekly phishing attempts with 99.4% success rate. This work contributes practical solutions for protecting email infrastructure from advanced phishing threats while maintaining computational efficiency and user experience.
Navneet Yadav (Sun,) studied this question.