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In the model development phase, we designed a phishing detection model that combines optimized feature selection with a strong ensemble classification strategy to improve detection efficiency and accuracy. The goal is to ensure that only the most relevant features are used while leveraging the strengths of multiple classifiers.
The model is built around three main components. First, automated feature selection is performed using a Genetic Algorithm, which helps identify the most informative phishing-detection features in the dataset. Compared to traditional filter-and-wrapper methods, the GA provides better optimization by balancing exploration and exploitation within the feature search space. Second, the selected features are processed using ensemble classifiers, allowing multiple models to learn from the data and improve prediction performance. Finally, a majority voting mechanism is applied to combine the predictions from the ensemble models, ensuring more stable and consistent final classification results. |
IEEE/ICACT20260061 Slide.11
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