DocumentCode
623969
Title
A novel method to detect bad data injection attack in smart grid
Author
Ting Liu ; Yun Gu ; Dai Wang ; Yuhong Gui ; Xiaohong Guan
Author_Institution
Key Lab. for Intell. Networks & Network Security, Xi´an Jiaotong Univ., Xian, China
fYear
2013
fDate
14-19 April 2013
Firstpage
3423
Lastpage
3428
Abstract
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.
Keywords
IEEE standards; electric power generation; power engineering computing; power system security; power system state estimation; security of data; smart power grids; APSE; Chi-squares method; IEEE 39-bus system; adaptive partitioning state estimation; bad data injection attack detection mechanism; device breakdown; power generation; power system; sensitivity; smart grid; subsystem-extension; testing result; Conferences; Decision support systems; adaptive partitioning state estimation; bad data injection; detection; security; smart grid;
fLanguage
English
Publisher
ieee
Conference_Titel
INFOCOM, 2013 Proceedings IEEE
Conference_Location
Turin
ISSN
0743-166X
Print_ISBN
978-1-4673-5944-3
Type
conf
DOI
10.1109/INFCOM.2013.6567175
Filename
6567175
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