A META-ENSEMBLE LEARNING FRAMEWORK FOR ROBUST PHISHING WEBSITE DETECTION
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Abstract
Phishing attacks continue to pose a major cyber security threat by deceiving users into revealing sensitive information through fraudulent websites. Traditional detection approaches, such as blacklist and heuristic-based methods, are limited in detecting zero-day attacks. This paper proposes a meta-ensemble learning framework that integrates multiple machine learning models to enhance phishing website detection. The proposed approach combines traditional classifiers, advanced boosting models, and multi-level ensemble strategies to achieve high accuracy and robustness. Experimental results demonstrate that the meta-ensemble model outperforms individual and conventional ensemble methods, achieving an accuracy of 97.31% and F1-score of 0.9729
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