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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 …