Name
Title | ||
|---|---|---|
Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIFS.2016.2607701 | IEEE Trans. Information Forensics and Security |
Keywords | Field | DocType |
Smart grids,Topology,Security,Power system faults,Power system protection | Topology,Smart grid,Computer science,Vulnerability assessment,Computer network,Electric power system,Q-learning,Network topology,Power-system protection,Grid,Vulnerability,Distributed computing | Journal |
Volume | Issue | ISSN |
12 | 1 | 1556-6013 |
Citations | PageRank | References |
12 | 0.54 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Yan | 1 | 179 | 13.72 |
| Haibo He | 2 | 3653 | 213.96 |
| Xiangnan Zhong | 3 | 346 | 16.35 |
| Yufei Tang | 4 | 203 | 22.83 |