| Title | Reinforcement Learning Based Vulnerability Analysis of Data Injection Attack for Smart Grids |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Luo, Weifeng, Xiao, Liang |
| Conference Name | 2021 40th Chinese Control Conference (CCC) |
| Keywords | Benchmark testing, data injection attacks, Estimation, measurement uncertainty, Meters, Metrics, power grid vulnerability analysis, Power measurement, pubcrawl, reinforcement learning, resilience, Resiliency, Scalability, simulation, Smart grid, vulnerability analysis |
| Abstract | Smart grids have to protect meter measurements against false data injection attacks. By modifying the meter measurements, the attacker misleads the control decisions of the control center, which results in physical damages of power systems. In this paper, we propose a reinforcement learning based vulnerability analysis scheme for data injection attack without relying on the power system topology. This scheme enables the attacker to choose the data injection attack vector based on the meter measurements, the power system status, the previous injected errors and the number of meters to compromise. By combining deep reinforcement learning with prioritized experience replay, the proposed scheme more frequently replays the successful vulnerability detection experiences while bypassing the bad data detection, which is able to accelerate the learning speed. Simulation results based on the IEEE 14 bus system show that this scheme increases the probability of successful vulnerability detection and reduce the number of meters to compromise compared with the benchmark scheme. |
| DOI | 10.23919/CCC52363.2021.9550523 |
| Citation Key | luo_reinforcement_2021 |
Reinforcement Learning Based Vulnerability Analysis of Data Injection Attack for Smart Grids