Related Experiment Video
Updated: May 5, 2026
03:31
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
Published on: December 15, 2023
1.3K
Generative Adversarial Networks for Intrusion Detection Systems: A Comprehensive Survey of Applications, Challenges,
Mohammad Alauthman1, Nauman Aslam2, Ahmad Al-Qerem3
1Department of Information Security, University of Petra, Amman, Jordan.
Arabian Journal for Science and Engineering
|February 23, 2026
Summary
Generative Adversarial Networks (GANs) enhance intrusion detection systems by creating synthetic attack data and improving anomaly detection. This survey reviews GAN applications, architectures, and emerging trends for robust, adaptive cybersecurity defenses.
Area of Science:
- Cybersecurity and Artificial Intelligence
- Network Intrusion Detection Systems
- Machine Learning Applications
Background:
- The increasing complexity of cyber threats necessitates adaptive intrusion detection systems (IDS).
- Generative Adversarial Networks (GANs) show promise in improving IDS performance through synthetic data generation and anomaly detection.
- Existing surveys lack comprehensive coverage of GANs in diverse environments like IoT and federated systems.
Purpose of the Study:
- To systematically review and analyze the current research landscape of GAN-based intrusion detection systems (IDS).
- To extend the scope beyond traditional networks to include Internet of Things (IoT), federated, and software-defined networking (SDN) environments.
- To propose a unified evaluation framework and discuss emerging paradigms and open challenges.
Main Methods:
- Comprehensive literature review of GAN-based IDS research.
- Analysis of various GAN architectures (e.g., Wasserstein GANs, conditional GANs, self-attention GANs).
- Examination of applications, datasets, evaluation metrics, and deployment scenarios (IoT, federated, SDN).
Main Results:
- GANs effectively generate synthetic attack traffic, balance datasets, enhance robustness, and detect anomalies.
- Lightweight and tabular GANs are suitable for resource-constrained IoT and edge devices.
- A unified evaluation framework is proposed, emphasizing interpretable and multi-modal approaches.
Conclusions:
- GANs offer significant potential for developing robust, adaptive, and privacy-preserving intrusion detection systems.
- Future research should focus on addressing GAN training challenges, exploring novel architectures (e.g., quantum GANs, diffusion models), and ensuring ethical deployment.
- Interpretable and multi-modal GAN-based IDS are crucial for effectively fusing diverse data sources for enhanced threat detection.
