# A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes

arXiv: https://arxiv.org/abs/2407.08839 · HTML: https://mlsanity.com/paper/2407.08839/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 106 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 30 of 35)

- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [Technical Report on the CleverHans v2.1.0 Adversarial Examples Library](https://mlsanity.com/paper/1610.00768/) (2016)
- [OpenAttack: An Open-source Textual Adversarial Attack Toolkit](https://mlsanity.com/paper/2009.09191/) (2020)
- [DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses](https://mlsanity.com/paper/2005.06149/) (2020)
- [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https://mlsanity.com/paper/1702.05983/) (2017)
- [Task-Agnostic Detector for Insertion-Based Backdoor Attacks](https://mlsanity.com/paper/2403.17155/) (2024)
- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision](https://mlsanity.com/paper/2203.03762/) (2022)
- [A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis](https://mlsanity.com/paper/2204.03804/) (2022)
- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [EvilModel 2.0: Bringing Neural Network Models into Malware Attacks](https://mlsanity.com/paper/2109.04344/) (2021)
- [DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans](https://mlsanity.com/paper/2401.15354/) (2024)
- [Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation](https://mlsanity.com/paper/2208.09779/) (2022)
- [TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification](https://mlsanity.com/paper/2206.09319/) (2022)
- [A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices](https://mlsanity.com/paper/2005.04970/) (2020)
- [Generative AI for Cyber Threat-Hunting in 6G-enabled IoT Networks](https://mlsanity.com/paper/2303.11751/) (2023)
- [Balanced Training for Sparse GANs](https://mlsanity.com/paper/2302.14670/) (2023)
- [Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection](https://mlsanity.com/paper/2403.02232/) (2024)
- [G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System](https://mlsanity.com/paper/2006.00676/) (2020)
- [Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks](https://mlsanity.com/paper/2206.09349/) (2022)
- [RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture](https://mlsanity.com/paper/2311.16018/) (2023)
- [Mapping New Realities: Ground Truth Image Creation with Pix2Pix Image-to-Image Translation](https://mlsanity.com/paper/2404.19265/) (2024)
- [Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans](https://mlsanity.com/paper/2010.00641/) (2020)
- [Deperturbation of Online Social Networks via Bayesian Label Transition](https://mlsanity.com/paper/2010.14121/) (2020)
- [Perigee: Efficient Peer-to-Peer Network Design for Blockchains](https://mlsanity.com/paper/2006.14186/) (2020)
- [Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks](https://mlsanity.com/paper/2010.13308/) (2020)
- [Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks](https://mlsanity.com/paper/2106.14344/) (2021)
- [EVAGAN: Evasion Generative Adversarial Network for Low Data Regimes](https://mlsanity.com/paper/2109.08026/) (2021)
- [Robust Node Representation Learning via Graph Variational Diffusion Networks](https://mlsanity.com/paper/2312.10903/) (2023)

## Built on by (most cited first, 2 of 2)

- [Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation](https://mlsanity.com/paper/2509.20411/) (2025)
- [Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)](https://mlsanity.com/paper/2508.05591/) (2025)

## Abstract (arXiv)

With the proliferation of Artificial Intelligence, there has been a massive increase in the amount of data required to be accumulated and disseminated digitally. As the data are available online in digital landscapes with complex and sophisticated infrastructures, it is crucial to implement various defense mechanisms based on cybersecurity. Generative Adversarial Networks (GANs), which are deep learning models, have emerged as powerful solutions for addressing the constantly changing security issues. This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses. Our survey aims to explore the various works completed in GANs, such as Intrusion Detection Systems (IDS), Mobile and Network Trespass, BotNet Detection, and Malware Detection. The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains. Further, the paper discusses the challenges and constraints of using GANs in these areas and suggests future research directions. Overall, the paper highlights the potential of GANs in enhancing cybersecurity measures and addresses the need for further exploration in this field.