摘要： 针对智能电网高级量测体系（AMI）与计算机网络互联通信中存在的安全威胁，提出一种基于改进在线序列简化极核极限学习机（DBN-OS-RKELM）的AMI入侵检测算法。将采集到的历史网络日志数据通过深度信念网络进行重要特征提取，并在特征学习过程中实现高维数据的低维表示以减少冗余特征，同时将当前新到达的网络日志数据添加到DBN-OS-RKELM网络中进行输出权重的实时更新，从而完成AMI入侵检测的分类。实验结果表明，与基于极限学习机和在线序列极限学习机等的入侵检测算法相比，基于DBN-OS-RKELM的入侵检测算法具有更好的泛化能力与更快的学习速率，且提高了入侵检测准确率。

关键词: 高级量测体系, 深度信念网络, 极限学习机, 在线学习, 入侵检测

Abstract: To address the security threats in the communication between computer network and the Advanced Metering Infrastructure(AMI) for smart grid,this paper proposes an improved intrusion detection algorithm for AMI based on DBN-OS-RKELM.This algorithm uses Deep Belief Network(DBN) to extract the main features of collected historical network log data,and presents the high-dimensional data in a low-dimensional form during the feature learning to reduce redundant features.Then the newly arrived network log data is added to DBN-OS-RKELM to update the output weight in real time,so as to complete the classification of intrusion detection for AMI.Experimental results show that compared with intrusion detection algorithms based on Extreme Learning Machine(ELM),Online Sequential Extreme Learning Machine(OS-ELM) and so on,the proposed intrusion detection algorithm based on DBN-OS-RKELM has a better generalization ability and faster learning rate,and improves the accuracy of intrusion detection.

Key words: Advanced Metering Infrastructure(AMI), Deep Belief Network(DBN), Extreme Learning Machine(ELM), online learning, intrusion detection

中图分类号: 

• TP393