Objectives . This review article sets out to evaluate the use of Generative Adversarial Networks (GANs) to revolutionize cybersecurity and anomaly detection process. The research focuses in particular on the capabilities of GANs to produce synthetic data and simulate adversarial attacks, as well as identifying outliers and resolving training, instability, and ethical issues. Methods . A systematic review of relevant peer-reviewed articles spanning 2014 through 2024 was undertaken. Results . The discussion concentrated on two main areas of GAN application: (1) cybersecurity through intrusion detection and adversarial testing; (2) anomaly detection for medical diagnostics and surveillance purposes. The research studied two essential GAN variants named Wasserstein GANs and Conditional GANs for their performance in addressing technical challenges. The assessment of synthetic data quality used the Fréchet Inception Distance and Structural Similarity Index Measure as evaluation metrics. Conclusions . GANs enhance security measures through their production of caused datasets resulting in a 25% improvement of detection systems accuracy. The technique allows strong adversarial assessment to reveal system weaknesses while helping detect irregularities in data-poor areas for medical diagnostics. High-dimensional tasks demonstrate 40% training instability and lead to 30% output diversity loss. The need for regulatory frameworks becomes essential due to ethical issues, which include the use of deepfakes that result in 25% success rates of biometric system evasion. Given ethical rules regulating their proper use, GANs advance cybersecurity by providing anomaly detection simultaneously with improved training stability and lower operating expenses. Prior versions of GAN-reinforcement learning and additional transparent systems require focused development as part of responsible innovation efforts.
Arafat et al. (2025) studied this question.