Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems

• URL: http://arxiv.org/abs/2505.03120v1
• Date: Tue, 06 May 2025 02:27:17 GMT
• Title: Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems
• Authors: Abdul Mustafa, Muhammad Talha Khan, Muhammad Azmi Umer, Zaki Masood, Chuadhry Mujeeb Ahmed,
• Abstract summary: We generate adversarial samples using the Jacobian Saliency Map Attack (JSMA)<n>We validate the generalization and scalability of the adversarial samples to tackle a broad range of real attacks on Industrial Control Systems.<n>The model trained with adversarial samples detected attacks with 95% accuracy on real-world attack data not used during training.
• Score: 2.6513941799808873
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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