Affiliation:
1. Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan
Abstract
Future engineering systems with new capabilities that far exceed today’s levels of autonomy, functionality, usability, dependability, and cyber security are predicted to be designed and developed using cyber-physical systems (CPSs). In this paper, the security of CPSs is investigated through a case study of a smart grid by using a reinforcement learning (RL) augmented attack graph to effectively highlight the subsystems’ weaknesses. In particular, the state action reward state action (SARSA) RL technique is used, in which the agent is taken to be the attacker, and an attack graph created for the system is built to resemble the environment. SARSA uses rewards and penalties to identify the worst-case attack scenario; with the most cumulative reward, an attacker may carry out the most harm to the system with the fewest available actions. Results showed successfully the worst-case attack scenario with a total reward of 26.9 and identified the most severely damaged subsystems.
Funder
Deanship of Graduate Studies and Scientific Research at the German Jordanian University
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献