Generative Adversarial Networks have emerged as powerful tools for cybersecurity applications, offering capabilities ranging from synthetic security data generation to adversarial attack simulation and defense. d defense. This paper presents a comprehensive analysis of GAN-based approaches for cybersecurity, examining their application in three key areas: synthetic attack data generation for training security systems, adversarial attack simulation for evaluating system robustness, and GAN-based defense mechanisms for detecting and mitigating cyber threats. We evaluate different GAN architectures includin g DCGANs, Wasserstein GANs, and conditional GANs for security data generation, assessing the quality and utility of generated samples for training intrusion detection systems. Our experimental evaluation demonstrates that GAN-generated training data improves detection of rare attack categories by 34% while GAN-based defense mechanisms reduce adversarial evasion success rates by 61%. We also analyze the security risks of GANs themselves, including susceptibility to mode collapse and potential for misuse in generating sophisticated attack traffic.
Safa et al. (Sun,) studied this question.