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Wide and Recurrent Neural Networks for Detection of False Data Injection in Smart Grids

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Wireless Algorithms, Systems, and Applications (WASA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11604))

Abstract

A smart grid is a complex system using power transmission and distribution networks to connect electric power generators to consumers across a large geographical area. Due to their heavy dependencies on information and communication technologies, smart grid applications, such as state estimation, are vulnerable to various cyber-attacks. False data injection attacks (FDIA), considered as the most severe threats for state estimation, can bypass conventional bad data detection mechanisms and render a significant threat to smart grids. In this paper, we propose a novel FDIA detection mechanism based on a wide and recurrent neural networks (RNN) model to address the above concerns. Simulations over IEEE 39-bus system indicate that the proposed mechanism can achieve a satisfactory FDIA detection accuracy.

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Acknowledgment

This work was partially supported by the US National Science Foundation under grant IIS-1741279, and the National Science Foundation of China under grants 61832012, 61771289, and 61672321.

Author information

Authors and Affiliations

  1. Department of Computer Science, The George Washington University, Washington DC, USA

    Yawei Wang, Cheng Zhang & Xiuzhen Cheng

  2. College of Information and Science Technology, Beijing Normal University, Beijing, People’s Republic of China

    Donghui Chen

  3. GEIRI North America, San Jose, CA, USA

    Xi Chen

  4. School of Information Science and Engineering, Qufu Normal University, Rizhao, Shandong, People’s Republic of China

    Baogui Huang

Authors
  1. Yawei Wang
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  2. Donghui Chen
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  3. Cheng Zhang
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  4. Xi Chen
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  5. Baogui Huang
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  6. Xiuzhen Cheng
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Corresponding author

Correspondence to Cheng Zhang .

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Editors and Affiliations

  1. University of Hawaii at Manoa, Honolulu, HI, USA

    Edoardo S. Biagioni

  2. University of Hawaii at Manoa, Honolulu, USA

    Yao Zheng

  3. Harbin Institute of Technology, Harbin, China

    Siyao Cheng

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Wang, Y., Chen, D., Zhang, C., Chen, X., Huang, B., Cheng, X. (2019). Wide and Recurrent Neural Networks for Detection of False Data Injection in Smart Grids. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_27

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  • DOI: https://doi.org/10.1007/978-3-030-23597-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23596-3

  • Online ISBN: 978-3-030-23597-0

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