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A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine

Author

Listed:
  • Ke Zhang

    (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Science and Technology on Eletronic Information Control Laboratory, Chengdu 610000, China)

  • Zhi Hu

    (School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Yufei Zhan

    (Glasgow College, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Xiaofen Wang

    (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Keyi Guo

    (Courant Institute of Mathematical Science, New York University, New York, NY 10003, USA)

Abstract

The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.

Suggested Citation

  • Ke Zhang & Zhi Hu & Yufei Zhan & Xiaofen Wang & Keyi Guo, 2020. "A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine," Energies, MDPI, vol. 13(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4907-:d:415871
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    References listed on IDEAS

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    1. Eun-Seop Yu & Jae-Min Cha & Taekyong Lee & Jinil Kim & Duhwan Mun, 2019. "Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network," Energies, MDPI, vol. 12(23), pages 1-19, November.
    2. Jianlei Gao & Senchun Chai & Baihai Zhang & Yuanqing Xia, 2019. "Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis," Energies, MDPI, vol. 12(7), pages 1-17, March.
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    Cited by:

    1. Evangelos Syrmos & Vasileios Sidiropoulos & Dimitrios Bechtsis & Fotis Stergiopoulos & Eirini Aivazidou & Dimitris Vrakas & Prodromos Vezinias & Ioannis Vlahavas, 2023. "An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements," Sustainability, MDPI, vol. 15(3), pages 1-20, January.
    2. Tehseen Mazhar & Hafiz Muhammad Irfan & Sunawar Khan & Inayatul Haq & Inam Ullah & Muhammad Iqbal & Habib Hamam, 2023. "Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods," Future Internet, MDPI, vol. 15(2), pages 1-37, February.

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