Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems

TL;DR

This paper presents a method for generating adversarial samples to improve the robustness of intrusion detection systems in industrial control systems, demonstrating high detection accuracy on real attack data.

Contribution

It introduces the use of Jacobian Saliency Map Attack (JSMA) for creating adversarial samples tailored for ICS intrusion detection, enhancing model resilience against real-world attacks.

Findings

Achieved 95% attack detection accuracy on unseen real-world data.

Validated the scalability of adversarial samples across various attack types.

Demonstrated effectiveness on a secure water treatment testbed.

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.