Likelihood Analysis of Cyber Data Attacks to Power Systems With Markov Decision Processes
Cyber data attacks are the worst-case interacting bad data to power system state estimation and cannot be detected by existing bad data detectors. This paper for the first time analyzes the likelihood of cyber data attacks by characterizing the actions of a malicious intruder in a dynamic environment, where power system state evolves with time, and measurement devices could become inaccessible due to detected attacks. This analysis is important for the operator to evaluate the vulnerability of power systems to data attacks. A Markov decision process is proposed to model an intruder’s strategy, with the objective to maximize its cumulative reward across time. The optimal attack policy is solved from the intruder’s perspective, and the attack likelihood is then analyzed based on the obtained policy. Two attack scenarios are studied to model different knowledge levels of the intruder about the dynamics of power systems. Numerical experiments are conducted on the IEEE 14-bus and 30-bus test systems to study the intruder’s attack strategy and analyze the attack probability.