From Hardening to Understanding: Adversarial Training vs. CF-Aug for Explainable Cyber-Threat Detection System

Abstract

Machine Learning (ML) intrusion detection systems (IDS) are vulnerable to manipulations: small, protocol-valid manipulations can push samples across brittle decision boundaries. We study two complementary remedies that reshape the learner in distinct ways. Adversarial Training (AT) exposes the model to worst-case, in-threat perturbations during learning to thicken local margins; Counterfactual Augmentation (CF-Aug) adds near-boundary exemplars that are explicitly constrained to be feasible, causally consistent, and operationally meaningful for defenders. The main goal of this work is to investigate and compare how AT and CF-Aug can reshape the decision surface of the IDS. eXplainable Artificial Intelligence (XAI) is used to analyze the shifts in global feature importance stability under both AT and CF perturbation to link these shifts to the accuracy of the IDS in detecting cyber-threats. This yields a clear picture when boundary hardening (AT) or boundary sculpting (CF-Aug) better serves IDS. Two well-known techniques are used to generate adversarial samples, namely the Fast Gradient Sign Method (FGSM) and the Projected Gradient Descent (PGD) techniques. We have achieved better accuracy with AT and CF-Aug compared to the baseline IDS.