Title

Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks

Document Type

Article

Date of Original Version

1-1-2017

Abstract

Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis.

Publication Title

IEEE Transactions on Information Forensics and Security

Volume

12

Issue

1

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