Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation
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Abstract (arXiv)
Machine learning-based cybersecurity systems are highly vulnerable to adversarial attacks, while Generative Adversarial Networks (GANs) act as both powerful attack enablers and promising defenses. This survey systematically reviews GAN-based adversarial defenses in cybersecurity (2021--August 31, 2025), consolidating recent progress, identifying gaps, and outlining future directions. Using a PRISMA-compliant systematic literature review protocol, we searched five major digital libraries. From 829 initial records, 185 peer-reviewed studies were retained and synthesized through quantitative trend analysis and thematic taxonomy development. We introduce a four-dimensional taxonomy spanning defensive function, GAN architecture, cybersecurity domain, and adversarial threat model. GANs improve detection accuracy, robustness, and data utility across network intrusion detection, malware analysis, and IoT security. Notable advances include WGAN-GP for stable training, CGANs for targeted synthesis, and hybrid GAN models for improved resilience. Yet, persistent challenges remain such as instability in training, lack of standardized benchmarks, high computational cost, and limited explainability. GAN-based defenses demonstrate strong potential but require advances in stable architectures, benchmarking, transparency, and deployment. We propose a roadmap emphasizing hybrid models, unified evaluation, real-world integration, and defenses against emerging threats such as LLM-driven cyberattacks. This survey establishes the foundation for scalable, trustworthy, and adaptive GAN-powered defenses.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 37 of its 472 references that are in the map. First posted on arXiv 24 Sep 2025.
Builds on
37 of 472 references.
References · 37
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25 more
- 2024Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilitiescited by 21
- 2025EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifierscited by 19
- 2024Generative AI for Secure Physical Layer Communications: A Surveycited by 17
- 2023Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection Systemcited by 8
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