TY - GEN
T1 - Invited Paper
T2 - 25th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2023
AU - Morgenstern, Gal
AU - Dabush, Lital
AU - Kim, Jip
AU - Anderson, James
AU - Zussman, Gil
AU - Routtenberg, Tirza
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - False data injection (FDI) attacks pose a significant threat to the reliability of power system state estimation (PSSE). Recently, graph signal processing (GSP)-based detectors have been shown to enable the detection of well-designed cyber attacks named unobservable FDI attacks. However, current detectors, including GSP-based detectors, do not consider the impact of secured sensors on the detection process; thus, they may have limited power, especially in the low signal-to-noise ratio (SNR) regime. In this paper, we propose a novel FDI attack detection method that incorporates both knowledge of the locations of secured sensors and the GSP properties of power system states (voltages). We develop the secured-sensors-and-graph-Laplacian-based generalized likelihood ratio test (SSGL-GLRT) that integrates the secured data and the graph smoothness properties of the state variables. Furthermore, we introduce a generalization of the method that allows the use of different high-pass GSP filters together with prior knowledge of the locations of the secured sensors. Then, we develop the SSGL-GLRT for a distributed PSSE based on the alternating direction method of multipliers (ADMM). Numerical simulations demonstrate that the proposed method significantly improves the probability of detecting FDI attacks compared to existing GSP-based detectors, achieving an increase of up to 30% in the detection probability for the same false alarm rate by integrating secured sensor location information.
AB - False data injection (FDI) attacks pose a significant threat to the reliability of power system state estimation (PSSE). Recently, graph signal processing (GSP)-based detectors have been shown to enable the detection of well-designed cyber attacks named unobservable FDI attacks. However, current detectors, including GSP-based detectors, do not consider the impact of secured sensors on the detection process; thus, they may have limited power, especially in the low signal-to-noise ratio (SNR) regime. In this paper, we propose a novel FDI attack detection method that incorporates both knowledge of the locations of secured sensors and the GSP properties of power system states (voltages). We develop the secured-sensors-and-graph-Laplacian-based generalized likelihood ratio test (SSGL-GLRT) that integrates the secured data and the graph smoothness properties of the state variables. Furthermore, we introduce a generalization of the method that allows the use of different high-pass GSP filters together with prior knowledge of the locations of the secured sensors. Then, we develop the SSGL-GLRT for a distributed PSSE based on the alternating direction method of multipliers (ADMM). Numerical simulations demonstrate that the proposed method significantly improves the probability of detecting FDI attacks compared to existing GSP-based detectors, achieving an increase of up to 30% in the detection probability for the same false alarm rate by integrating secured sensor location information.
KW - Graph signal processing (GSP)
KW - cyber-physical systems
KW - distributed detection
KW - false data injection (FDI) attack detection
KW - power system state estimation (PSSE)
KW - secured sensors
UR - http://www.scopus.com/inward/record.url?scp=85174534421&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-44274-2_18
DO - 10.1007/978-3-031-44274-2_18
M3 - Conference contribution
SN - 9783031442735
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 240
EP - 258
BT - Stabilization, Safety, and Security of Distributed Systems - 25th International Symposium, SSS 2023, Proceedings
A2 - Dolev, Shlomi
A2 - Schieber, Baruch
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 October 2023 through 4 October 2023
ER -