Quickest Detection of Time-Varying False Data Injection Attacks in Dynamic Smart Grids
Quickest detection of false data injection attacks (FDIAs) in dynamic smart grids is considered in this paper. The unknown time-varying state variables of the smart grid and the FDIAs impose a significant challenge for designing a computationally efficient detector. To address this challenge, we propose new Cumulative-Sum-type algorithms with computational complex scaling linearly with the number of meters. Moreover, for any constraint on the expected false alarm period, a lower bound on the threshold employed in the proposed algorithm is provided. For any given threshold employed in the proposed algorithm, an upper bound on the worstcase expected detection delay is also derived. The proposed algorithm is numerically investigated in the context of an IEEE standard power system under FDIAs, and is shown to outperform some representative algorithm in the test case.
J. Zhang, "Quickest Detection of Time-Varying False Data Injection Attacks in Dynamic Smart Grids," Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (2019, Brighton, United Kingdom), pp. 2432-2436, Institute of Electrical and Electronics Engineers (IEEE), May 2019.
The definitive version is available at https://doi.org/10.1109/ICASSP.2019.8683102
2019 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019 (2019: May 12-17, Brighton, United Kingdom)
Electrical and Computer Engineering
Keywords and Phrases
Cumulative sum; Cybersecurity; Dynamic smart grid systems; False data injection attacks
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 May 2019