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These results demonstrate that the proposed validation strategy successfully reflects real-world deployment conditions while maintaining high evaluation reliability. The experimental results show excellent performance during both training and validation across the two phishing datasets tested.
As shown in the confusion matrices, the model achieved perfect classification performance for both datasets. For Dataset 1, all phishing and legitimate websites were correctly classified, resulting in 750 true positives and 750 true negatives, with zero false positives and zero false negatives. Similarly, for Dataset 2, the model achieved 15,142 true positives and 20,227 true negatives, again with zero false positives and zero false negatives.
These results indicate that the proposed hybrid ensemble model is highly effective in distinguishing phishing websites from legitimate ones. The absence of misclassification errors suggests strong generalization capability and robustness of the model across datasets with different characteristics. |
IEEE/ICACT20260061 Slide.16
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