用梯度方法生成对抗样本，提升工业控制系统异常检测能力。
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems
- 基于雅可比显著性图攻击生成对抗样本。
- 模型在真实攻击数据上达到95%检测准确率。
- 适合研究工业控制安全与对抗防御的学者。
基于机器学习的入侵检测系统（IDS）易受对抗攻击影响。为使IDS在恶意实体利用前具备识别对抗样本的能力，本文采用雅可比显著性图攻击（JSMA）生成对抗样本。通过在实际运行的网络安全水处理（SWaT）测试平台上的实验，验证了对抗样本的泛化性和可扩展性，可应对多种真实攻击场景。评估表明，使用对抗样本训练的模型在未参与训练的真实攻击数据上实现了95%的检测准确率。
原文摘要 · Abstract (English)
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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