# Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization

arXiv: https://arxiv.org/abs/1910.14107 · HTML: https://mlsanity.com/paper/1910.14107/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective](https://mlsanity.com/paper/2101.03042/) (2021)
- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks](https://mlsanity.com/paper/2108.09199/) (2021)
- [DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning](https://mlsanity.com/paper/2101.03218/) (2021)
- [Evasion Adversarial Attacks Remain Impractical Against ML-based Network Intrusion Detection Systems, Especially Dynamic Ones](https://mlsanity.com/paper/2306.05494/) (2023)
- [A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS](https://mlsanity.com/paper/2409.07448/) (2024)
- [Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness](https://mlsanity.com/paper/2403.10461/) (2024)
- [Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks](https://mlsanity.com/paper/2603.10413/) (2026)
- [A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?](https://mlsanity.com/paper/2605.18666/) (2026)

## Abstract (arXiv)

With the growth of adversarial attacks against machine learning models, several concerns have emerged about potential vulnerabilities in designing deep neural network-based intrusion detection systems (IDS). In this paper, we study the resilience of deep learning-based intrusion detection systems against adversarial attacks. We apply the min-max (or saddle-point) approach to train intrusion detection systems against adversarial attack samples in NSW-NB 15 dataset. We have the max approach for generating adversarial samples that achieves maximum loss and attack deep neural networks. On the other side, we utilize the existing min approach [2] [9] as a defense strategy to optimize intrusion detection systems that minimize the loss of the incorporated adversarial samples during the adversarial training. We study and measure the effectiveness of the adversarial attack methods as well as the resistance of the adversarially trained models against such attacks. We find that the adversarial attack methods that were designed in binary domains can be used in continuous domains and exhibit different misclassification levels. We finally show that principal component analysis (PCA) based feature reduction can boost the robustness in intrusion detection system (IDS) using a deep neural network (DNN).