An Advanced Meta-Learning Ensemble Framework for Interpretable Phishing Detection via RSTHFS Optimization

Phishing remains a dominant and highly sophis- ticated form of cybercrime, where attackers deploy deceptive websitestotrickusersintorevealingsensitiveinformation, such as passwords and financial credentials [1], [5]. Despite significant advancements in cybersecurity, accurately detecting these malicious domains remains a critical challenge due to the lack of universally accepted identification parameters and the rapid emergence of ”zero-day” phishing sites [1], [6]. This paper introduces an advanced detection framework that integrates Rough Set Theory-based Hybrid Feature Selection (RSTHFS) withanInnovativeMeta-Learning-BasedEnsembleapproach[3], [6]. The proposed methodology utilizes a multi-layer stacking architecturetocapturebothglobalnon-linearandlocalpatterns, leveraging base learners such as Residual Multi-Layer Percep- trons (ResMLP) and XGBoost, which are aggregated by a metaclassifier to enhance predictive stability [1], [3]. To ensure the system is lightweight enough for real-time browser deployment, the RSTHFS method is employed to identify a ”minimal reduct” of features, successfully reducing the computational featurespace by over 60% while maintaining high reliability [5], [6]. Furthermore, the framework incorporates Explainable AI (XAI) through SHAP values to provide granular transparency into the model’s decision-making process [6]. Experimental evaluations on benchmark datasets demonstrate a peak accuracy of 98.4%, providing a scalable, efficient, and interpretable solution for modern web security [3], [5].