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Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid

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  • Yuancheng Li
  • Rixuan Qiu
  • Sitong Jing

Abstract

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can’t satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

Suggested Citation

  • Yuancheng Li & Rixuan Qiu & Sitong Jing, 2018. "Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
    • Handle: RePEc:plo:pone00:0192216
    DOI: 10.1371/journal.pone.0192216
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    Cited by:

    1. Dongqi Wang & Mingshuo Nie & Dongming Chen, 2023. "BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder," Mathematics, MDPI, vol. 11(15), pages 1-14, August.

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