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Genetic Algorithm-Based Cumulative Sum Method for Jamming Attack Detection of Cyber-Physical Power Systems

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.