DocumentCode
621180
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
Minist. of Educ. Key Lab. for Intell. Networks & Network Security, Xi´an Jiaotong Univ., Xian, China
fYear
2013
fDate
14-19 April 2013
Firstpage
49
Lastpage
54
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
power engineering computing; power system protection; power system security; smart power grids; APSE; IEEE 39-bus system; adaptive partitioning state estimation; bad data detection mechanism; bad data injection attack; power generation; smart grid; Algorithm design and analysis; Estimation; Indexes; Pipelines; Weight measurement; adaptive partitioning state estimation; bad data injection; detection; security; smart grid;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Communications Workshops (INFOCOM WKSHPS), 2013 IEEE Conference on
Conference_Location
Turin
Print_ISBN
978-1-4799-0055-8
Type
conf
DOI
10.1109/INFCOMW.2013.6562907
Filename
6562907
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