Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation

2025-09-24

AI Summary

Generative Adversarial Networks (GANs) exhibit a dual role in cybersecurity—both as enablers of adversarial attacks and as promising tools for defense—yet their systematic assessment remains fragmented. Method: This study conducts a PRISMA-compliant systematic literature review (2021–2025.08), analyzing 185 peer-reviewed papers. Contribution/Results: We propose a novel four-dimensional taxonomy encompassing defensive functionality, GAN architecture, security domain, and threat model. Our analysis reveals advances in training stability and task specificity via WGAN-GP, CGAN, and hybrid architectures, and—first among comprehensive reviews—systematically maps GAN-based defenses against LLM-centric threats. Empirical findings indicate that GANs significantly improve accuracy, robustness, and few-shot generalization in intrusion detection, malware analysis, and IoT security. However, critical challenges persist, including training instability, lack of standardized benchmarks, and limited interpretability. This work delivers a structured knowledge graph and actionable research directions for GAN-driven cyber defense.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Adversarial Learning & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Security and Privacy: Cyber-crime defenses and forensics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Defending machine learning cybersecurity systems against adversarial attacks
• Systematically reviewing GAN-based defense methods across cybersecurity domains
• Addressing training instability and lack of benchmarks in GAN defenses

Innovation

• GANs enhance detection accuracy and robustness
• WGAN-GP and CGANs improve training and targeted synthesis
• Hybrid GAN models increase resilience against cyber threats