Knowledge Commons of Institute of Automation,CAS
Stealthy false data injection attacks against extended Kalman filter detection in power grids | |
Liu, Yifa1,2![]() ![]() | |
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 |
DOI | 10.1109/ICCSS53909.2021.9721954 |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
是否为代表性论文 | 否 |
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 | 否 |
引用统计 | |
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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Stealthy_False_Data_(1623KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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