Uncertainty-Aware Evidential Adversarial Defense for Cloud Intrusion Detection under GAN-Based Evasion Attacks

Abstract

Cloud intrusion detection systems (IDSs) increasingly depend on deep neural classifiers, which are vulnerable to adversarial evasion attacks—including realistic, GAN-crafted traffic—that flip malicious flows to benign with near-imperceptible, functionally consistent perturbations. Adversarial training (AT) hardens the decision boundary but yields overconfident point predictions: a hardened detector still cannot signal when an input is suspicious, and Bayesian or ensemble uncertainty estimators that could do so require many forward passes, which is impractical at cloud line rate. We propose UA-EAD, an uncertainty-aware evidential adversarial defense that (i) equips the detector with an evidential (Dirichlet) head yielding calibrated predictive uncertainty in a single forward pass, (ii) trains it with an uncertainty-weighted adversarial objective plus a consistency regularizer that concentrates robustness on the most uncertain, near-boundary flows, and (iii) uses the resulting uncertainty for selective prediction, abstaining on inputs it cannot confidently classify. On NSL-KDD under FGSM, BIM, PGD, C&W, and a black-box WGAN-GP transfer attack, UA-EAD matches the strongest AT baselines in robust accuracy (97.4%) while attaining the highest clean accuracy (98.1%). Its single-pass evidential uncertainty equals maximum-softmax-probability and surpasses Monte-Carlo Dropout for adversarial detection at 21x lower inference cost, and yields the best adversarial-detection AUROC among robust models. Selective prediction lifts accuracy on 17 novel (out-of-distribution) attack types from 76.9% to 87.0% at 70% coverage. We further report an empirical robustness–detectability trade-off that clarifies why hardening alone is insufficient for trustworthy cloud IDS.