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.
@article{arxiv.2505.03120,
title = {Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems},
author = {Abdul Mustafa and Muhammad Talha Khan and Muhammad Azmi Umer and Zaki Masood and Chuadhry Mujeeb Ahmed},
journal= {arXiv preprint arXiv:2505.03120},
year = {2025}
}
Comments
Accepted in the 1st Workshop on Modeling and Verification for Secure and Performant Cyber-Physical Systems in conjunction with Cyber-Physical Systems and Internet-of-Things Week, Irvine, USA, May 6-9, 2025