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In this study, we propose a hybrid machine learning framework to improve the accuracy of phishing website detection. The framework integrates three main components, which are feature selection, ensemble learning, and a majority voting mechanism. By combining these techniques, the model is able to produce more reliable and stable detection results compared to single-model approaches.
The proposed model is specifically designed to address common challenges in phishing detection, such as imbalanced datasets, redundant or irrelevant features, and the continuously evolving nature of phishing attack strategies. The overall methodology is structured into four main phases: data preprocessing, model development, training, and evaluation. Each phase plays an important role in ensuring that the final detection system is robust, adaptable, and suitable for real-world cybersecurity applications. |
IEEE/ICACT20260061 Slide.09
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