Exploiting GANs Against IDSs: A Systematic Review, Meta-Analysis, and Case Study Evaluation

Researchers are using GANs (generative adversarial networks, AI systems that create synthetic data by having two neural networks compete against each other) to generate sophisticated adversarial attacks that can fool IDSs (intrusion detection systems, software that monitors networks for suspicious activity). This review examines how GAN-based attacks compromise IDS security and identifies which GAN variants are most effective at creating realistic attacks that evade detection.