期刊
IEEE TRANSACTIONS ON SMART GRID
卷 6, 期 6, 页码 2725-2735出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2014.2374577
关键词
Cyber security; distributed algorithm; generalized CUSUM; level-triggered sampling; smart grid quickest detection; wide-area monitoring
资金
- U.S. National Science Foundation [CIF1064575]
- U.S. Office of Naval Research [N000141410667]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1064575] Funding Source: National Science Foundation
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1405327] Funding Source: National Science Foundation
We consider the sequential (i.e., online) detection of false data injection attacks in smart grid, which aims to manipulate the state estimation procedure by injecting malicious data to the monitoring meters. The unknown parameters in the system, namely the state vector, injected malicious data and the set of attacked meters pose a significant challenge for designing a robust, computationally efficient, and high-performance detector. We propose a sequential detector based on the generalized likelihood ratio to address this challenge. Specifically, the proposed detector is designed to be robust to a variety of attacking strategies, and load situations in the power system, and its computational complexity linearly scales with the number of meters. Moreover, it considerably outperforms the existing first-order cumulative sum detector in terms of the average detection delay and robustness to various attacking strategies. For wide-area monitoring in smart grid, we further develop a distributed sequential detector using an adaptive sampling technique called level-triggered sampling. The resulting distributed detector features single bit per sample in terms of the communication overhead, while preserving the high performance of the proposed centralized detector.
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