Cascading Failure Attacks in the Power System: A Stochastic Game Perspective

Abstract

Electric power systems are critical infrastructure and are vulnerable to contingencies including natural disasters, system errors, malicious attacks, etc. These contingencies can affect the world's economy and cause great inconvenience to our daily lives. Therefore, security of power systems has received enormous attention for decades. Recently, the development of the Internet of Things (IoT) enables power systems to support various network functions throughout the generation, transmission, distribution, and consumption of energy with IoT devices (such as sensors, smart meters, etc.). On the other hand, it also incurs many more security threats. Cascading failures, one of the most serious problems in power systems, can result in catastrophic impacts such as massive blackouts. More importantly, it can be taken advantage by malicious attackers to launch physical or cyber attacks on the power system. In this paper, we propose and investigate cascading failure attacks (CFAs) from a stochastic game perspective. In particular, we formulate a zerosum stochastic attack/defense game for CFAs while considering the attack/defense costs, budget constraints, diverse load shedding costs, and dynamic states in the system. Then, we develop a Q-CFA learning algorithm that works efficiently in power systems without any a priori information. We also formally prove that the convergence of the proposed algorithm achieves a Nash equilibrium. Simulation results validate the efficacy and efficiency of the proposed scheme by comparisons with other state-of-the-art approaches.