Attack Detection for Cyber-Physical Systems Based on Causal Representation
提出因果表示学习框架，通过提取抗操纵的低维因果特征来检测信息物理系统中的隐蔽攻击，并给出检测概率和虚警控制的理论保证，实验优于传统统计和深度学习方法。
Cyber-physical systems (CPSs) are increasingly vulnerable to sophisticated cyber-attacks due to their growing complexity and connectivity. Current detection approaches face significant challenges in distinguishing stealthy attacks from normal operational variations, as adversaries can craft attacks that preserve statistical correlations while disrupting physical processes. To address this critical gap, we propose a novel causal representation learning (CRL) framework that leverages intrinsic physical invariants for robust attack detection in industrial CPS. We develop two complementary methodologies, unsupervised secure causal feature extraction (USCFE) and supervised secure causal feature extraction (SSCFE), which extract low-dimensional causal features resilient to adversarial manipulation. Furthermore, theoretical guarantees on detection probability bounds and false alarm control are provided. Experimental validation shows that proposed methods outperform both traditional statistical methods and contemporary deep learning approaches, thus significantly advancing CPS security.