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
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;
Conference_Titel :
INFOCOM, 2013 Proceedings IEEE
Conference_Location :
Turin
Print_ISBN :
978-1-4673-5944-3
DOI :
10.1109/INFCOM.2013.6567175