Stealthy false data injection attacks against extended Kalman filter detection in power grids
Liu, Yifa1,2; Cheng, Long1,2
2021-12
会议日期2021.12.10-12
会议地点Beijing, China
关键词False data injection, state estimation, extended Kalman filter, attack sequence
DOI10.1109/ICCSS53909.2021.9721954
英文摘要

The power grid is a kind of national critical infrastructure directly affiliated to human daily life. Because of the vital functions and potentially significant losses, the power grid becomes an excellent target for many malicious attacks. Due to the special nonlinear measurements, many detection methods do not match the grid very well. The extended Kalman filter based detection is one of the few methods suitable for nonlinear system detection, and therefore can be used in power system to spot malicious attacks. However, the reliability and effectiveness of the extended Kalman filter based detection have not been fully studied and adequately guaranteed. By proposing a two-step false data injection attack strategy, this paper gives a stealthy way to inject false data of increasing magnitude into the power grid, which can eventually cause a certain degree of deviation of the grid state without being detected. In the simulation, the method proposed in this paper caused a voltage deviation of more than 25% before being discovered in the power system.

会议录Proceedings of 2021 8th International Conference on Information, Cybernetics, and Computational Social Systems
语种英语
URL标识查看原文
内容类型会议论文
源URL[http://ir.ia.ac.cn/handle/173211/52242]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Cheng, Long
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Liu, Yifa,Cheng, Long. Stealthy false data injection attacks against extended Kalman filter detection in power grids[C]. 见:. Beijing, China. 2021.12.10-12.
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