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InfoResearchPeer-reviewed
Exploiting GANs Against IDSs: A Systematic Review, Meta-Analysis, and Case Study Evaluation
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Summary
This article presents a systematic review and meta-analysis of research on GAN-based adversarial attacks against intrusion detection systems (IDSs). The authors empirically test binary and multiclass IDS models under GAN-based attacks, examining the influence of feature selection. They assess IDS performance and sample realism using Fréchet inception distance and Hellinger distance, and identify the most severe GAN architecture and the one that generates the most realistic adversarial samples.