A Hybrid Framework for Real-Time Phishing Detection Using URL, Content, and DOM Features with Interpretable ML

Abstract: There is a new wave of increasingly sophisticated phishing, necessitating sophisticated detection systems which combine many aspects for accuracy and real time functionality. To improve detection robustness this research proposes a scalable hybrid system for real-time detection of phishing, taking into consideration URL-based attributes, content attributes, plus DOM structure attributes as a three-dimensional approach to URL-based phishing detection. Our approach incorporates explainable machine learning techniques (SHAP) and ensemble models (SVM-Deep Learning) to provide good accuracy as well as support for security analysts and their decisions. The system uses adaptive learning and dynamic feature selection to remain robust against evolving techniques in phishing. The experimental caried with many datasets, and proposed approach outperforms traditional single-feature approaches while achieving an exceptionally low false positive rate of 0.7%, with 98.3% detection accuracy. This research makes it feasible to connect high-performing Artificial Intelligence with meaningful cybersecurity practicalities, especially regarding a current, relevant, scalable, and real-time response to today's phishing attacks, while seamlessly adopting it to web browsers and security gateways.