Identification of Successive “Unobservable” Cyber Data Attacks in Power Systems Through Matrix Decomposition
Identification of Successive “Unobservable” Cyber Data Attacks in Power Systems Through Matrix Decomposition
复制标题
通过矩阵分解识别电力系统中连续的“不可观察”网络数据攻击
DOI：
10.1109/tsp.2016.2597131
复制
发表时间：
2016
影响因子：
5.4
通讯作者：
Michael P. Razanousky
中科院分区：
文献类型：
--
作者：
Pengzhi Gao;Meng Wang;J. Chow;Scott G. Ghiocel;B. Fardanesh;G. Stefopoulos;Michael P. Razanousky
本文提出了一种新的框架，识别一系列的网络数据攻击的电力系统同步相量测量。我们专注于检测“不可观察”的网络数据攻击，这些攻击无法通过任何纯粹依赖于一个时刻接收到的测量值的现有方法检测到。利用相量测量单元（PMU）数据的近似低秩特性，我们将连续不可观测网络攻击的识别问题表示为低秩矩阵加上变换列稀疏矩阵的矩阵分解问题。提出了一种基于凸优化的数据辨识方法，并给出了其在数据辨识中的理论保证。通过对中央纽约电力系统PMU实测数据和人工合成数据的仿真实验，验证了该方法的有效性。
This paper presents a new framework of identifying a series of cyber data attacks on power system synchrophasor measurements. We focus on detecting “unobservable” cyber data attacks that cannot be detected by any existing method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the identification problem of successive unobservable cyber attacks as a matrix decomposition problem of a low-rank matrix plus a transformed column-sparse matrix. We propose a convex-optimization-based method and provide its theoretical guarantee in the data identification. Numerical experiments on actual PMU data from the Central New York power system and synthetic data are conducted to verify the effectiveness of the proposed method.