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Stealthy false data injection attacks against extended Kalman filter detection in power grids
Liu, Yifa1,2; Cheng, Long1,2
2021-12
会议名称2021 8th International Conference on Information, Cybernetics, and Computational Social Systems
会议录名称Proceedings of 2021 8th International Conference on Information, Cybernetics, and Computational Social Systems
会议日期2021.12.10-12
会议地点Beijing, China
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.

关键词False data injection, state estimation, extended Kalman filter, attack sequence
学科门类工学::控制科学与工程
DOI10.1109/ICCSS53909.2021.9721954
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收录类别EI
语种英语
是否为代表性论文
七大方向——子方向分类复杂系统理论与方法
国重实验室规划方向分类复杂系统建模与推演
是否有论文关联数据集需要存交
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文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/52242
专题复杂系统认知与决策实验室_先进机器人
多模态人工智能系统全国重点实验室
通讯作者Cheng, Long
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
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],2021.
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