Likelihood of Cyber Data Injection Attacks to Power Systems
For power system operators, this work provides a framework to assess the risk of undetectable cyber attacks, though it is an incremental application of existing methods to a known problem.
This paper analyzes the likelihood of cyber data injection attacks on power system state estimation by modeling an intruder's strategy as a Markov decision process and using linear programming to find optimal attack policies. Numerical experiments on test systems demonstrate the effectiveness of the approach.
Cyber data attacks are the worst-case interacting bad data to power system state estimation and cannot be detected by existing bad data detectors. In this paper, we for the first time analyze the likelihood of cyber data attacks by characterizing the actions of a malicious intruder. We propose to use Markov decision process to model an intruder's strategy, where the objective is to maximize the cumulative reward across time. Linear programming method is employed to find the optimal attack policy from the intruder's perspective. Numerical experiments are conducted to study the intruder's attack strategy in test power systems.