Evaluating the robustness of explainable AI methods to adversarial attacks in intrusion and malware detection

Summary

This paper evaluates how two gradient-based untargeted evasion attacks, FGSM and PGD, affect the stability of DALEX, SHAP and Integrated Gradients explanations in intrusion and malware detection. Explanation drift is reported separately for inputs whose predicted label changes and those where it is preserved. Experiments on CICMalMem22 and CICIDS17 show measurable drift when predictions are preserved, with mean Top-5 overlap between 0.586 and 0.892, and the authors conclude that XAI is an additional attack surface.

Topics

• Adversarial machine learning

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