• 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