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To address the limitations identified in previous approaches, this study proposes a hybrid phishing detection model designed to improve both detection performance and reliability. The model begins by applying a Genetic Algorithm to identify the most significant and relevant features from the dataset. These selected features are then validated using a Random Forest model to ensure their effectiveness in classification.
Next, the optimized features are fed into three different boosting-based classifiers, namely XGBoost, AdaBoost, and Real AdaBoost. Each classifier provides a different learning perspective, which helps strengthen the overall detection capability of the system.
To ensure stable and consistent predictions, the outputs from these classifiers are combined using a majority voting mechanism. The proposed hybrid model demonstrated strong performance and robustness when tested across two phishing datasets with different configurations.
Overall, this approach helps address common challenges in phishing detection, including overfitting, excessive or irrelevant features, and prediction inconsistency across models. |
IEEE/ICACT20260061 Slide.08
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