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.

翻译：基于机器学习的网络安全系统极易受到对抗性攻击，而生成对抗网络（GANs）既可作为强大的攻击赋能工具，也可作为极具前景的防御手段。本综述系统回顾了网络安全中基于GAN的对抗性防御研究（2021年至2025年8月31日），整合了最新进展，识别了研究空白，并展望了未来方向。采用符合PRISMA标准的系统文献综述方法，我们检索了五个主要数字图书馆。从829条初始记录中，最终保留并综合分析了185项同行评议研究，通过定量趋势分析和主题分类框架构建进行归纳。我们提出了一个四维分类体系，涵盖防御功能、GAN架构、网络安全领域和对抗性威胁模型。GAN在网络入侵检测、恶意软件分析和物联网安全等领域提升了检测精度、鲁棒性和数据效用。显著进展包括：采用WGAN-GP实现稳定训练、利用CGAN进行定向合成，以及通过混合GAN模型增强系统韧性。然而，持续存在的挑战包括训练不稳定性、标准化基准缺失、高昂计算成本及可解释性有限。基于GAN的防御技术展现出巨大潜力，但需要在稳定架构、基准测试、透明度和实际部署方面取得突破。我们提出了一个发展路线图，强调混合模型、统一评估体系、现实场景集成以及针对新兴威胁（如LLM驱动的网络攻击）的防御策略。本综述为构建可扩展、可信赖且自适应的GAN驱动防御体系奠定了基础。