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False data separation for data security in smart grids

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Abstract

The smart grid is emerging as an efficient paradigm for electric power generation, transmission, and consumption, based on optimized decision making and control that leverage the measurement data of sensors and meters in the grid. False data injection is a new type of power grid attacks aiming to tamper such important data. For the security and robustness of the grid, it is critical to separate the false data injected by such attacks and recover the original measurement data. Nonetheless, the existing approaches often neglect the true changes on original measurement data that are caused by the real perturbations on grid states and hence have a risk of removing these true changes as injected false data during the data recovery. In this paper, we preserve these true changes by modeling the false data problem as a rank-bounded \(L_1\) norm optimization and propose both offline and online algorithms to filter out the injected false data and recover original measurement data. Trace-driven simulations verify the efficacy of our solution.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61502347, 61502504, and 61628209), the Nature Science Foundation of Hubei Province of China (2016CFB384 and 2016CFA030), the Fundamental Research Funds for the Central Universities (2042015kf0038), the Research Funds of Renmin University of China (15XNLF09), and the Research Funds for Introduced Talents of Wuhan University.

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Authors and Affiliations

  1. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China

    Hao Huang & Qian Yan

  2. Department of Computer Science, University of Calgary, Calgary, Canada

    Yao Zhao & Zongpeng Li

  3. School of Information and DEKE, MOE, Renmin University of China, Beijing, China

    Wei Lu

  4. School of Computing, National University of Singapore, Singapore, Singapore

    Zhenguang Liu

  5. School of Computer, Wuhan University, Wuhan, China

    Zongpeng Li

Authors
  1. Hao Huang
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  2. Qian Yan
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  3. Yao Zhao
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  4. Wei Lu
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  5. Zhenguang Liu
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  6. Zongpeng Li
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Correspondence to Wei Lu.

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Huang, H., Yan, Q., Zhao, Y. et al. False data separation for data security in smart grids. Knowl Inf Syst 52, 815–834 (2017). https://doi.org/10.1007/s10115-016-1019-8

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  • DOI: https://doi.org/10.1007/s10115-016-1019-8

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