X-MAP: eXplainable Misclassification Analysis and Profiling for Spam and Phishing Detection
This addresses the harmful impact of false negatives and positives in spam and phishing detection for users and systems, offering an incremental improvement with interpretability.
The paper tackles the problem of misclassifications in spam and phishing detection by introducing X-MAP, a framework that identifies topic-level semantic patterns behind model failures, achieving up to 0.98 AUROC and recovering up to 97% of falsely rejected correct predictions.
Misclassifications in spam and phishing detection are very harmful, as false negatives expose users to attacks while false positives degrade trust. Existing uncertainty-based detectors can flag potential errors, but possibly be deceived and offer limited interpretability. This paper presents X-MAP, an eXplainable Misclassification Analysis and Profilling framework that reveals topic-level semantic patterns behind model failures. X-MAP combines SHAP-based feature attributions with non-negative matrix factorization to build interpretable topic profiles for reliably classified spam/phishing and legitimate messages, and measures each message's deviation from these profiles using Jensen-Shannon divergence. Experiments on SMS and phishing datasets show that misclassified messages exhibit at least two times larger divergence than correctly classified ones. As a detector, X-MAP achieves up to 0.98 AUROC and lowers the false-rejection rate at 95% TRR to 0.089 on positive predictions. When used as a repair layer on base detectors, it recovers up to 97% of falsely rejected correct predictions with moderate leakage. These results demonstrate X-MAP's effectiveness and interpretability for improving spam and phishing detection.