Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 71, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2022.3186360
Keywords
Jamming; Detectors; Genetic algorithms; Pollution measurement; Time measurement; Power system stability; Power measurement; Cumulative sum; cyber-physical power systems (CPPSs); distributed algorithm; genetic algorithm; jamming attack
Funding
- National Natural Science Foundation of China [U1966202, 61972288]
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This article proposes genetic-algorithm-based cumulative sum methods for online detection of jamming attacks in cyber-physical power systems (CPPSs). The proposed detectors are robust to time-varying jamming magnitudes and time-varying attacked locations, and show effectiveness in detecting jamming attacks in CPPSs.
Jamming attack is one of the common malicious attacks in cyber-physical power systems (CPPSs) to intervene measurements and systems. Timely detection of jamming attacks is crucial for real-time monitoring and security of CPPSs. With this goal, this article proposes genetic-algorithm-based cumulative sum methods for online detection of jamming attacks in both centralized and distributed CPPSs. The proposed detectors are robust to time-varying jamming magnitudes and time-varying attacked locations. The time-varying attack magnitudes are estimated by a maximum likelihood estimation (MLE). The attacked locations are determined by a binary-coded genetic algorithm (BCGA), where the locations are encoded into a binary string. Moreover, in the distributed setting, we propose two parallel information filters to estimate states in the case of jamming attacks. One distributed and four centralized IEEE bus systems are used to testify the proposed detectors. The numerical results show the effectiveness of the proposed detectors in detecting jamming attacks in CPPSs.
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