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To further evaluate the robustness of our classifier, we analyze its performance through two critical diagnostic curves. On the left, our Precision-Recall curve maintains a high level of precision across nearly all recall values, achieving an Average Precision of 0.9772. This is a vital result because it proves the classifier remains robust and reliable even in the face of potential class imbalances, ensuring we do not sacrifice accuracy to catch more phishing attempts. On the right, the Receiver Operating Characteristic, or ROC curve, shows near-ideal performance with an AUC value of 0.9867. The curves sharp ascent toward the top-left corner demonstrates an exceptional True Positive Rate paired with an extremely low False Positive Rate. These results collectively confirm that our hybrid BiLSTM and hEVOSWO model is not just accurate on paper, but possesses superior discriminative power for real-world deployment. |
IEEE/ICACT20260103 Slide.18
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