Generative Adversarial Networks for Cybersecurity Applications: From Synthetic Data Generation to Adversarial Defense

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.