# IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection

arXiv: https://arxiv.org/abs/1809.02077 · HTML: https://mlsanity.com/paper/1809.02077/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 30 of 42)

- [TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack](https://mlsanity.com/paper/2103.06297/) (2021)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [On the Robustness of Domain Constraints](https://mlsanity.com/paper/2105.08619/) (2021)
- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [Impacts and Risk of Generative AI Technology on Cyber Defense](https://mlsanity.com/paper/2306.13033/) (2023)
- [A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System](https://mlsanity.com/paper/2312.03245/) (2023)
- [Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning](https://mlsanity.com/paper/2107.05127/) (2021)
- [Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning](https://mlsanity.com/paper/2309.03598/) (2023)
- [SoK: On the Offensive Potential of AI](https://mlsanity.com/paper/2412.18442/) (2024)
- [Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications](https://mlsanity.com/paper/2510.21887/) (2025)
- [Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks](https://mlsanity.com/paper/2107.14776/) (2021)
- [Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review](https://mlsanity.com/paper/2206.02733/) (2022)
- [Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection](https://mlsanity.com/paper/2210.02840/) (2022)
- [Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.07003/) (2023)
- [DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware](https://mlsanity.com/paper/2102.00898/) (2021)
- [FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic](https://mlsanity.com/paper/2203.11106/) (2022)
- [ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator](https://mlsanity.com/paper/2109.11542/) (2021)
- [Improving the quality of generative models through Smirnov transformation](https://mlsanity.com/paper/2110.15914/) (2021)
- [NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks](https://mlsanity.com/paper/2202.09873/) (2022)
- [DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection](https://mlsanity.com/paper/2301.03368/) (2023)
- [A Malware Classification Survey on Adversarial Attacks and Defences](https://mlsanity.com/paper/2312.09636/) (2023)
- [Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods](https://mlsanity.com/paper/2402.15617/) (2024)
- [Transfer Learning for Security: Challenges and Future Directions](https://mlsanity.com/paper/2403.00935/) (2024)
- [Deep generative models as an adversarial attack strategy for tabular machine learning](https://mlsanity.com/paper/2409.12642/) (2024)
- [Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability](https://mlsanity.com/paper/2505.01328/) (2025)
- [Quantum Gated Recurrent GAN with Gaussian Uncertainty for Network Anomaly Detection](https://mlsanity.com/paper/2510.26487/) (2025)
- [Using EBGAN for Anomaly Intrusion Detection](https://mlsanity.com/paper/2206.10400/) (2022)
- [Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems](https://mlsanity.com/paper/2207.04843/) (2022)
- [Optimizing the AI Development Process by Providing the Best Support Environment](https://mlsanity.com/paper/2305.00136/) (2023)

## Abstract (arXiv)

As an essential tool in security, the intrusion detection system bears the responsibility of the defense to network attacks performed by malicious traffic. Nowadays, with the help of machine learning algorithms, intrusion detection systems develop rapidly. However, the robustness of this system is questionable when it faces adversarial attacks. For the robustness of detection systems, more potential attack approaches are under research. In this paper, a framework of the generative adversarial networks, called IDSGAN, is proposed to generate the adversarial malicious traffic records aiming to attack intrusion detection systems by deceiving and evading the detection. Given that the internal structure and parameters of the detection system are unknown to attackers, the adversarial attack examples perform the black-box attacks against the detection system. IDSGAN leverages a generator to transform original malicious traffic records into adversarial malicious ones. A discriminator classifies traffic examples and dynamically learns the real-time black-box detection system. More significantly, the restricted modification mechanism is designed for the adversarial generation to preserve original attack functionalities of adversarial traffic records. The effectiveness of the model is indicated by attacking multiple algorithm-based detection models with different attack categories. The robustness is verified by changing the number of the modified features. A comparative experiment with adversarial attack baselines demonstrates the superiority of our model.