A Novel Method to Detect Bad Data Injection Attack in Smart Grid

被引：0

作者：

Lin, Ting ^{[1
]}

Gu, Yun ^{[1
]}

Wang, Dai ^{[1
]}

Gui, Yuhong ^{[1
]}

Guan, Xiaohong ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Minist Educ, Key Lab Intelligent Networks & Network Secur, Sch Elect & Informat Engn, Xian, Shaanxi, Peoples R China

来源：

2013 PROCEEDINGS IEEE INFOCOM | 2013年

关键词：

smart grid; security; detection; bad data injection; adaptive partitioning state estimation;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Bad data injection is one of most dangerous attacks in smart grid, as it might lead to energy theft on the end users and device breakdown on the power generation. The attackers can construct the bad data evading the bad data detection mechanisms in power system. In this paper, a novel method, named as Adaptive Partitioning State Estimation (APSE), is proposed to detect bad data injection attack. The basic ideas are: 1) the large system is divided into several subsystems to improve the sensitivity of bad data detection; 2) the detection results are applied to guide the subsystem updating and re-partitioning to locate the bad data. Two attack cases are constructed to inject bad data into an IEEE 39-bus system, evading the traditional bad data detection mechanism. The experiments demonstrate that all bad data can be detected and located within a small area using APSE.

引用

页码：3423 / 3428

页数：6

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