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The concept of smart home has recently gained significant popularity. Despite that it offers improved convenience and cost reduction, the prevailing smart home infrastructure suffers from vulnerability due to cyberattacks. It is possible for hackers to launch cyberattacks at the community level while causing a large area power system blackout through cascading effects. In this paper, the cascading impacts of two cyberattacks on the predicted dynamic electricity pricing are analyzed. In the first cyberattack, the hacker manipulates the electricity price to form peak energy loads such that some transmission lines are overloaded. Those transmission lines are then tripped and the power system is separated into isolated islands due to the cascading effect. In the second cyberattack, the hacker manipulates the electricity price to increase the fluctuation of the energy load to interfere the frequency of the generators. The generators are then tripped by the protective procedures and cascading outages are induced in the transmission network. The existing technique only tackles overloading cyberattack while still suffering from the severe limitation in scalability. Therefore, based on partially observable Markov decision processes, a hierarchical detection framework exploring community decomposition and global policy optimization is proposed in this work. The simulation results demonstrate that our proposed hierarchical computing technique can effectively and efficiently detect those cyberattacks, achieving the detection accuracy of above 98%, while improving the scalability.
Liu et al. (Mon,) studied this question.