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To conclude our quantitative analysis, we present the Confusion Matrix, which offers a granular look at our models decision-making precision. As the matrix illustrates, our hybrid framework demonstrates exceptional clarity in distinguishing between classes. Out of our testing set, the model correctly identified 1,598 legitimate URLs and 1,585 phishing URLs. What is most impressive for a security application is the extremely low error rate; we recorded only 42 false positives and 46 false negatives. The fact that the vast majority of our predictions fall squarely on the diagonal confirms that our model achieves high accuracy while maintaining an incredibly low false-alarm rate. This level of reliability is exactly what is required for a real-world phishing detection system to be both effective and trusted by users. |
IEEE/ICACT20260103 Slide.19
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