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In the results phase, we conducted comprehensive experiments to evaluate how effectively the proposed model can detect phishing websites. The evaluation focuses on both validation and final testing to assess how accurately the model distinguishes between legitimate and phishing webpages.
For validation, 15% of each dataset was reserved and used as unseen data to test the model independently. To ensure fairness and avoid bias, the true labels were hidden from the model during prediction and were only revealed after all predictions were completed. This approach helps simulate real-world deployment conditions, ensuring that the evaluation results are reliable, unbiased, and representative of actual performance.
Overall, the testing process demonstrates the model¡¯s ability to generalize well and maintain strong detection accuracy when applied to new, unseen phishing data. |
IEEE/ICACT20260061 Slide.15
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