Towards Attack Resilient Data Analytics for Power Grid Operations
Sashini De Silva and Travis Hagan, Ph.D. students in the Department of Electrical Engineering and Computer Science at Oregon State University.
With an abundance of real-time data from a power grid, real-time data analytics is expected to revolutionize the way that critical decisions are made in power system operations. Nevertheless, employing data analytics for critical decision making may create a new attack surface unless a proper countermeasure is accompanied. In particular, an adversary may perturb power system operations by manipulating part of input data to the analytics. Therefore, an attack-resilient framework of designing and implementing such data analytics is necessary. In this talk, we will present an attack-resilient data analytics framework for power system protection. The proposed data analytics employs an artificial intelligence method equipped with power system simulator modules. The analytics processes real-time PMU data streams to compute an optimal protection decision in the presence of a GPS spoofing attack on PMUs. We will present the overview of our attack-resilient analytics and demonstrate its robustness against GPS spoofing attacks.