Explainable Robust Cloud Intrusion Detection Against Adversarial Evasion Using SHAP-Guided Feature Reweighting

Abstract

Cloud intrusion detection systems increasingly rely on machine learning classifiers trained on flow-level telemetry. Such classifiers can be accurate on clean traffic but fragile under adversarial evasion, because attackers may adjust mutable statistics such as timing, packet counts, or byte rates while preserving malicious functionality. This paper proposes SHAP-RW-AT, an explainable robust intrusion detection framework that combines adversarial augmentation with SHAP-guided feature reweighting. A base detector is first explained with SHAP values to identify decision-dominant features. These explanations are then combined with a domain mutability prior: highly important but easily manipulated flow statistics are attenuated, whereas stable high-importance indicators such as destination-port dispersion, failed-login behavior, and cloud API activity are reinforced. The reweighted representation is trained with diversified evasion examples generated by mean-shift, surrogate-gradient, and SHAP-aware perturbations under semantic constraints. Because public cloud-intrusion datasets could not be redistributed or downloaded inside the execution environment, we evaluate the method on a fully reproducible synthetic cloud-flow benchmark generated by the accompanying code; no result is claimed as a measurement on CICIDS-2017, UNSW-NB15, or CVD. Across three random seeds, SHAP-RW-AT obtains 99.67% clean accuracy and 99.71% average robust accuracy under three evasion strategies, reducing adversarial false negatives from 26.47% for a standard XGBoost detector to 0.18%. The results indicate that explanation-guided feature reweighting can act as a lightweight and auditable robustness mechanism for cloud IDS pipelines, while preserving traceability of the defensive decision logic.