# G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System

arXiv: https://arxiv.org/abs/2006.00676 · HTML: https://mlsanity.com/paper/2006.00676/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection](https://mlsanity.com/paper/2210.02840/) (2022)
- [A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes](https://mlsanity.com/paper/2407.08839/) (2024)
- [A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning](https://mlsanity.com/paper/2507.01976/) (2025)
- [Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue](https://mlsanity.com/paper/2203.11410/) (2022)
- [DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection](https://mlsanity.com/paper/2301.03368/) (2023)
- [HybridGuard: Enhancing Minority-Class Intrusion Detection in Dew-Enabled Edge-of-Things Networks](https://mlsanity.com/paper/2511.07793/) (2025)

## Abstract (arXiv)

The boundaries of cyber-physical systems (CPS) and the Internet of Things (IoT) are converging together day by day to introduce a common platform on hybrid systems. Moreover, the combination of artificial intelligence (AI) with CPS creates a new dimension of technological advancement. All these connectivity and dependability are creating massive space for the attackers to launch cyber attacks. To defend against these attacks, intrusion detection system (IDS) has been widely used. However, emerging CPS technologies suffer from imbalanced and missing sample data, which makes the training of IDS difficult. In this paper, we propose a generative adversarial network (GAN) based intrusion detection system (G-IDS), where GAN generates synthetic samples, and IDS gets trained on them along with the original ones. G-IDS also fixes the difficulties of imbalanced or missing data problems. We model a network security dataset for an emerging CPS using NSL KDD-99 dataset and evaluate our proposed model's performance using different metrics. We find that our proposed G-IDS model performs much better in attack detection and model stabilization during the training process than a standalone IDS.