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

arXiv: https://arxiv.org/abs/2509.20411 · HTML: https://mlsanity.com/paper/2509.20411/

## Key facts

- Cited by 1 papers 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 (most cited first, 30 of 37)

- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://mlsanity.com/paper/2101.04775/) (2021)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://mlsanity.com/paper/2006.05132/) (2020)
- [Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective](https://mlsanity.com/paper/2009.03728/) (2020)
- [A Systematic Survey of Regularization and Normalization in GANs](https://mlsanity.com/paper/2008.08930/) (2020)
- [Adversarial Machine Learning in Wireless Communications using RF Data: A Review](https://mlsanity.com/paper/2012.14392/) (2020)
- [Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities](https://mlsanity.com/paper/2405.12750/) (2024)
- [EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers](https://mlsanity.com/paper/2506.05074/) (2025)
- [Generative AI for Secure Physical Layer Communications: A Survey](https://mlsanity.com/paper/2402.13553/) (2024)
- [Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System](https://mlsanity.com/paper/2310.03334/) (2023)
- [MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors](https://mlsanity.com/paper/2011.01509/) (2020)
- [A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System](https://mlsanity.com/paper/2312.03245/) (2023)
- [Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis](https://mlsanity.com/paper/2203.02855/) (2022)
- [MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network](https://mlsanity.com/paper/2311.02378/) (2023)
- [Generative Adversarial Networks and Image-Based Malware Classification](https://mlsanity.com/paper/2207.00421/) (2022)
- [Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning](https://mlsanity.com/paper/2403.07078/) (2024)
- [Enhancing Network Intrusion Detection Performance using Generative Adversarial Networks](https://mlsanity.com/paper/2404.07464/) (2024)
- [A Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated Learning](https://mlsanity.com/paper/2405.11440/) (2024)
- [Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection](https://mlsanity.com/paper/2210.02840/) (2022)
- [Generative Adversarial Networks-Driven Cyber Threat Intelligence Detection Framework for Securing Internet of Things](https://mlsanity.com/paper/2304.05644/) (2023)
- [Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective](https://mlsanity.com/paper/2404.10796/) (2024)
- [Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI](https://mlsanity.com/paper/2405.18753/) (2024)
- [Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions](https://mlsanity.com/paper/2406.05443/) (2024)
- [A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes](https://mlsanity.com/paper/2407.08839/) (2024)

## Built on by (most cited first, 1 of 1)

- [Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach](https://mlsanity.com/paper/2605.08910/) (2026)

## 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.