PDL: An Efficient Prediction-Based False Data Injection Attack Detection and Location in Smart Grid

Wanjiao Shi, Yufeng Wang, Qun Jin, Jianhua Ma

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    With the rapid development of Internet of Things (IOT) technologies, modern power systems have become complex cyber-physical systems. A large number of smart devices have promoted efficient generation, transmission and distribution in the smart grid. State estimation (SE) is one of fundamental components in smart grid that evaluates the operation state of a grid by using a set of sensor measurements and grid topologies. A major issue is the authenticity of the measurements collected by the sensors. Specifically, the false data injection attack (FDIA) aims to temper the information that reflect the grid operation state. In this paper, we propose an efficient prediction-based FDIA detection and location scheme, PDL, in which the state vector of smart grid can be represented as multivariate time series, and can be predicted by vector autoregressive processes (VAR) through intentionally exploiting the temporal and spatial correlations of states. Different from most previous works which assumed the state transfer matrix constant and diagonal, a time-varying and non-diagonal matrix is adopted in this scheme. Then, the consistency between the predicted measurements and the observed measurements is utilized to detect and locate abnormal data. Besides, the detected abnormal data can be replaced with the predicted data, which simplifies the calibration process. Extensive simulation results verify the performance of the proposed scheme.

    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
    EditorsClaudio Demartini, Sorel Reisman, Ling Liu, Edmundo Tovar, Hiroki Takakura, Ji-Jiang Yang, Chung-Horng Lung, Sheikh Iqbal Ahamed, Kamrul Hasan, Thomas Conte, Motonori Nakamura, Zhiyong Zhang, Toyokazu Akiyama, William Claycomb, Stelvio Cimato
    PublisherIEEE Computer Society
    Pages676-681
    Number of pages6
    Volume2
    ISBN (Electronic)9781538626665
    DOIs
    Publication statusPublished - 2018 Jun 8
    Event42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan
    Duration: 2018 Jul 232018 Jul 27

    Other

    Other42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
    CountryJapan
    CityTokyo
    Period18/7/2318/7/27

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    Keywords

    • False data injection attack
    • Prediction
    • Smart grid
    • State estimation

    ASJC Scopus subject areas

    • Software
    • Computer Science Applications

    Cite this

    Shi, W., Wang, Y., Jin, Q., & Ma, J. (2018). PDL: An Efficient Prediction-Based False Data Injection Attack Detection and Location in Smart Grid. In C. Demartini, S. Reisman, L. Liu, E. Tovar, H. Takakura, J-J. Yang, C-H. Lung, S. I. Ahamed, K. Hasan, T. Conte, M. Nakamura, Z. Zhang, T. Akiyama, W. Claycomb, & S. Cimato (Eds.), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 (Vol. 2, pp. 676-681). [8377945] IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2018.10317