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

被引：26
|
作者：
Li, Yuancheng [1 ]
Qiu, Rixuan [1 ]
Jing, Sitong [1 ]
机构：
[1] North China Elect Power Univ, Dept Control & Comp Engn, Beijing, Peoples R China
来源：
PLOS ONE | 2018年 / 13卷 / 02期
关键词：
KEY MANAGEMENT SCHEME; SECURE COMMUNICATIONS; THEFT DETECTION; NETWORKS; ALGORITHM; ATTACK;
D O I：
10.1371/journal.pone.0192216
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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.
引用
收藏
页数：16
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