Detection of on-manifold adversarial attacks via latent space transformation
Year: 2025
Type: article
Abstract: 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.
Source: Computers & Security
Institutions: Edith Cowan University, Flinders University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,100
OpenAlex ID: W4408962310