• 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