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Physics-Aware Watermarking Embedded in Unknown Input Observers for False Data Injection Attack Detection in Cyber-Physical Microgrids
IEEE Transactions on Information Forensics and Security ( IF 6.3 ) Pub Date : 2024-08-21 , DOI: 10.1109/tifs.2024.3447235
Mengxiang Liu 1 , Xin Zhang 1 , Hengye Zhu 2 , Zhenyong Zhang 2 , Ruilong Deng 2
Affiliation  

The physics-aware watermarking-based detection method has shown great potential in detecting stealthy False Data Injection Attacks (FDIAs) by adding appropriate watermarks to control commands or sensor measurements, especially in industrial control systems and grid-tied Distributed Energy Resources (DERs). However, existing watermarking-based detection methods have limitations in either handling the intricate physical couplings among DERs or characterising the fast changing power electronics dynamics, and thus cannot be directly applied to microgrids. Inspired by the methodology of Unknown Input Observer (UIO), which can be employed for the distributed anomaly monitoring in cyber-physical microgrids but would be easily bypassed once the adversary has the knowledge of certain electrical parameters, this paper makes the first attempt to investigate the physics-aware watermarking embedded in UIOs such that the stealthy FDIAs would be intentionally disrupted by the watermarking scheme. Based on the theoretical analysis of the detection enhancement and performance degradation under watermarking-enhanced UIOs, the watermark strengths, UIO parameters, and control gains are optimally co-designed to significantly enhance the detection effectiveness while not degrading the control performance. The robustness of the watermarking-enhanced UIO to Time Synchronisation Errors (TSEs) is improved by employing a sliding time window with appropriate length. The performance of the proposed method is validated through Matlab/Simulink studies and cyber-physical co-simulation experiments, and the sensitivities of the detection latency and TSE robustness to watermark strength and detection window’s length are comprehensively studied.

中文翻译:


嵌入未知输入观察器中的物理感知水印,用于网络物理微电网中的虚假数据注入攻击检测



基于物理感知的水印检测方法通过添加适当的水印来控制命令或传感器测量,在检测隐秘的虚假数据注入攻击(FDIA)方面显示出巨大的潜力,特别是在工业控制系统和并网分布式能源(DER)中。然而,现有的基于水印的检测方法在处理分布式能源之间复杂的物理耦合或表征快速变化的电力电子动态方面存在局限性,因此不能直接应用于微电网。受未知输入观察者(UIO)方法的启发,该方法可用于网络物理微电网中的分布式异常监控,但一旦对手了解了某些电气参数,就很容易被绕过,本文首次尝试研究嵌入 UIO 中的物理感知水印,使得隐秘的 FDIA 会被水印方案故意破坏。基于水印增强UIO下检测增强和性能下降的理论分析,对水印强度、UIO参数和控制增益进行优化协同设计,在不降低控制性能的情况下显着提高检测效果。通过采用适当长度的滑动时间窗口,提高了水印增强的UIO对时间同步错误(TSE)的鲁棒性。通过Matlab/Simulink研究和信息物理联合仿真实验验证了该方法的性能,并综合研究了检测延迟和TSE鲁棒性对水印强度和检测窗口长度的敏感性。
更新日期:2024-08-21
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