Date Published: February 27, 2018
Publisher: Public Library of Science
Author(s): Yuancheng Li, Rixuan Qiu, Sitong Jing, Li Daqing.
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.
To achieve dynamic charging capability, Advanced Metering Infrastructure uses Smart Meters(SM), two-way communication system, Home Area Network(HAN) and Metering Data Management System(MDMS) to establish communication links with users . The communication process of AMI deploys a common communication protocol to meet the requirements of interconnection, which because the terminal devices of the client and part of the communication network are in an open form.
In this paper, we propose an intrusion detection system model based on the online sequential extreme learning machine for advanced measurement infrastructure. In the experiment, we use the gain ratio evaluation method to reduce the dimension of the sample dataset. The OS-ELM algorithm is used to classify and train datasets. Then a large number of experiments are conducted to select the optimal algorithm parameters for the proposed system. Finally, the proposed OS-ELM-based intrusion detection system is compared with other similar algorithms and the experimental results verify the effectiveness and feasibility of the proposed method.