Likelihood of Cyber Data Injection Attacks to Power Systems

TL;DR

This paper models the likelihood of cyber data injection attacks in power systems using Markov decision processes, providing insights into attack strategies and their detection challenges.

Contribution

It introduces a novel approach to analyze cyber attack likelihood in power systems by modeling intruder behavior with Markov decision processes.

Findings

Intruder strategies can be effectively modeled using Markov decision processes.

Optimal attack policies can be derived through linear programming.

Numerical experiments demonstrate the attack strategies in test systems.

Abstract

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.