# Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems

arXiv: https://arxiv.org/abs/2505.03120 · HTML: https://mlsanity.com/paper/2505.03120/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 13 of 13)

- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series](https://mlsanity.com/paper/2109.11428/) (2021)
- [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https://mlsanity.com/paper/1809.04758/) (2018)
- [Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks](https://mlsanity.com/paper/1806.08110/) (2018)
- [Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning](https://mlsanity.com/paper/1709.05342/) (2017)
- [Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems](https://mlsanity.com/paper/2012.15740/) (2020)
- [Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations](https://mlsanity.com/paper/2202.11917/) (2022)
- [Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning](https://mlsanity.com/paper/2107.05127/) (2021)
- [Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems](https://mlsanity.com/paper/1907.07487/) (2019)
- [Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities](https://mlsanity.com/paper/2101.12097/) (2021)

## Built on by (most cited first, 1 of 1)

- [Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems](https://mlsanity.com/paper/2508.04561/) (2025)

## Abstract (arXiv)

Machine learning (ML)-based intrusion detection systems (IDS) are vulnerable to adversarial attacks. It is crucial for an IDS to learn to recognize adversarial examples before malicious entities exploit them. In this paper, we generated adversarial samples using the Jacobian Saliency Map Attack (JSMA). We validate the generalization and scalability of the adversarial samples to tackle a broad range of real attacks on Industrial Control Systems (ICS). We evaluated the impact by assessing multiple attacks generated using the proposed method. The model trained with adversarial samples detected attacks with 95% accuracy on real-world attack data not used during training. The study was conducted using an operational secure water treatment (SWaT) testbed.