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The confusion matrix results show that for Dataset 1, the model achieved perfect classification performance, with no false positives and no false negatives. This demonstrates the model¡¯s strong capability when working with balanced datasets. For Dataset 2, the model achieved near-perfect performance, with only one false negative recorded. This indicates that the model is also effective in handling imbalanced datasets, which are common in real-world phishing detection scenarios.
Overall, these findings confirm that the proposed model achieves very high accuracy and reliability in detecting phishing URLs. The model outperforms many traditional detection approaches by leveraging ensemble learning combined with a majority voting strategy, which improves prediction stability and consistency.
In addition, the model uses only 27 highly discriminative features, which improves computational efficiency while maintaining strong detection performance. This also makes the model more robust against class imbalance, redundant features, and potential adversarial manipulation, making it suitable for real-world cybersecurity deployment. |
IEEE/ICACT20260061 Slide.18
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