Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems
This addresses security risks for industrial control systems, but it is incremental as it applies an existing adversarial attack method to a specific domain.
The paper tackled the vulnerability of machine learning-based intrusion detection systems to adversarial attacks in industrial control systems by generating adversarial samples using the Jacobian Saliency Map Attack, resulting in a model that detected attacks with 95% accuracy on real-world data.
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.