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In this proposed work, the study aims to address current challenges in phishing detection by introducing an ensemble-based detection system. The system combines intelligent feature selection techniques, high-performance machine learning models, and a reliable voting mechanism to improve overall detection accuracy and robustness.
The main contributions of this research include four key aspects. First, the study implements optimized feature selection to ensure only the most relevant phishing indicators are used for detection. Second, it designs a boosting-based ensemble model to enhance prediction performance. Third, it develops a reliable voting mechanism to improve decision consistency among multiple models. Finally, the proposed system is validated using multiple datasets to ensure its effectiveness and generalizability across different phishing scenarios. |
IEEE/ICACT20260061 Slide.04
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