| Title | False Data Injection Attack Detection Method Based on Residual Distribution of State Estimation |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Zhu, Lei, Huang, He, Gao, Song, Han, Jun, Cai, Chao |
| Conference Name | 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) |
| Keywords | command injection attacks, composability, distribution networks, feature extraction, Fitting, Metrics, Power systems, pubcrawl, resilience, Resiliency, Sensors, simulation, Uncertainty |
| Abstract | While acquiring precise and intelligent state sensing and control capabilities, the cyber physical power system is constantly exposed to the potential cyber-attack threat. False data injection (FDI) attack attempts to disrupt the normal operation of the power system through the coupling of cyber side and physical side. To deal with the situation that stealthy FDI attack can bypass the bad data detection and thus trigger false commands, a system feature extraction method in state estimation is proposed, and the corresponding FDI attack detection method is presented. Based on the principles of state estimation and stealthy FDI attack, we analyze the impacts of FDI attack on measurement residual. Gaussian fitting method is used to extract the characteristic parameters of residual distribution as the system feature, and attack detection is implemented in a sliding time window by comparison. Simulation results prove that the proposed attack detection method is effectiveness and efficiency. |
| Notes | ISSN: 2642-6633 |
| DOI | 10.1109/CYBER55403.2022.9907614 |
| Citation Key | zhu_false_2022 |
False Data Injection Attack Detection Method Based on Residual Distribution of State Estimation