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State estimation is a data processing algorithm for converting redundantmeter measurements and other information into an estimate of the state of apower system. Relying heavily on meter measurements, state estimation hasproven to be vulnerable to cyber attacks. In this paper, a novel targeted falsedata injection attack (FDIA) model against AC state estimation is proposed.Leveraging on the intrinsic load dynamics in ambient conditions and importantproperties of the Ornstein-Uhlenbeck process, we, from the viewpoint ofintruders, design an algorithm to extract power network parameters purely fromPMU data, which are further used to construct the FDIA vector. Requiring nonetwork parameters and relying only on limited phasor measurement unit (PMU)data, the proposed FDIA model can target specific states and launch largedeviation attacks. Sufficient conditions for the proposed FDIA model are alsodeveloped. Various attack vectors and attacking regions are studied in the IEEE39-bus system, showing that the proposed FDIA method can successfully bypassthe bad data detection and launch targeted large deviation attacks with veryhigh probabilities.

Targeted False Data Injection Attacks Against AC State Estimation Without Network Parameters