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[1]曾俊娆,李鹏,高莲,等.基于TNPE的智能电网虚假数据注入攻击检测*[J].中国安全生产科学技术,2021,17(3):124-129.[doi:10.11731/j.issn.1673-193x.2021.03.019]
　ZENG Junrao,LI Peng,GAO Lian,et al.Detection of false data injection attacks in smart grids based on time neighbor preserving embedding (TNPE)[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(3):124-129.[doi:10.11731/j.issn.1673-193x.2021.03.019]
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基于TNPE的智能电网虚假数据注入攻击检测*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
17
期数:
2021年3期
页码:
124-129
栏目:
职业安全卫生管理与技术
出版日期:
2021-03-31

文章信息/Info

Title:
Detection of false data injection attacks in smart grids based on time neighbor preserving embedding (TNPE)
文章编号:
1673-193X(2021)-03-0124-06
作者:
曾俊娆; 李鹏; 高莲; 沈鑫
（1.云南大学 信息学院，云南 昆明 650500；
2.云南省高校物联网技术及应用重点实验室，云南 昆明 650500;
3.云南电网有限责任公司 电力科学研究院，云南 昆明 650511)
Author(s):
ZENG Junrao;  LI Peng;  GAO Lian; SHEN Xin
(1.School of Information,Yunnan University,Kunming Yunnan 650500,China;
2.Internet of Things Technology and Application Key Laboratory of Universities in Yunnan,Kunming Yunnan 650500,China;
3.Yunnan Power Grid Co.,Ltd.,Kunming Yunnan 650511,China)
关键词:
电力系统; 状态估计; 时序近邻保持嵌入; 攻击检测
Keywords:
power system;  state estimation;  time neighbor preserving embedding;  attack detection
分类号:
X934
DOI:
10.11731/j.issn.1673-193x.2021.03.019
文献标志码:
A
摘要:
为实现智能电网中虚假数据注入攻击的实时检测，提高电力系统运行的安全性，采用1种基于时序近邻保持嵌入的方法，对正常状态下采集到的电网历史量测数据建立离线模型，得到T2统计限，将实时数据通过模型获得的T2统计量与离线模型的统计限进行对比，若超过统计限，则说明存在虚假数据注入攻击。该方法在提取局部空间结构特征的基础上，可同时获得与时间相关的动态特征。在IEEE30节点测试系统上进行仿真实验，并与ICA，PCA，NPE方法进行比较。结果表明：所提方法有高达100%的检测率，且有较低的误报率，能够有效应用在虚假数据注入攻击的检测中。
Abstract:
In order to realize the realtime detection of false data injection attacks in the smart grids and improve the safety of power system operation,a method based on the time neighbor preserving embedding (TNPE) was adopted,and an offline model was established for the historical measurement data of the power grid collected under normal conditions to obtain T2 statistical limit.The T2 statistics obtained through the model by the realtime data was compared with the T2 statistical limit of the offline model,and if the statistical limit was exceeded,it indicated that there was a false data injection attack.On the basis of extracting the local spatial structure features,this method could simultaneously obtain the timerelated dynamic features.The simulation experiment was carried out on the IEEE30 node test system and compared with the ICA,PCA,and NPE methods.The results showed that the method proposed in this paper had a detection rate of up to 100% and a low false alarm rate,which can be effectively applied in the detection of false data injection attacks.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期： 2020-12-08
* 基金项目： 国家自然科学基金项目(61763049)；云南省应用基础研究重点课题项目(2018FA032)
作者简介： 曾俊娆，硕士研究生，主要研究方向为智能电网攻击检测、电力大数据处理。
通信作者： 李鹏，博士，副教授，主要研究方向为智能电网运行和控制、电力信息物理融合系统。
更新日期/Last Update: 2021-04-13