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Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering
Yuancheng Li1, Rixuan Qiu1, Sitong Jing1
1Department of Control and Computer Engineering, North China Electric Power University, Beijing, China.
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.
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