Detection of on-manifold adversarial attacks via latent space transformation

Out-of-distribution (OOD) generalization is critical for reliable intrusion detection systems (IDS), yet current methods often falter against stealthy, on-manifold adversarial attacks that mimic ID data. To solve this challenge, we propose a semi-supervised approach that applies an invertible transformation to the latent space and leverages changes in differential entropy to detect OOD samples. Experiments on the KDD99 and X-IIoTID datasets demonstrate that our approach outperforms state-of-the-art defenses, providing enhanced robustness and generalizability for IDS.