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

17 Citations (Scopus)


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
Number of pages6
ISBN (Electronic)9781538626665
Publication statusPublished - 2018 Jun 8
Event42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan
Duration: 2018 Jul 232018 Jul 27

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157


Other42nd IEEE Computer Software and Applications Conference, COMPSAC 2018


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

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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