计算机工程 ›› 2019, Vol. 45 ›› Issue (10): 171-175,182.doi: 10.19678/j.issn.1000-3428.0052314

• 安全技术 • 上一篇    下一篇

基于DBN-KELM的入侵检测算法

汪洋, 伍忠东, 火忠彩   

  1. 兰州交通大学 电子与信息工程学院, 兰州 730070
  • 收稿日期:2018-08-06 修回日期:2018-10-04 出版日期:2019-10-15 发布日期:2019-10-14
  • 作者简介:汪洋(1994-),男,硕士研究生,主研方向为信息安全、深度学习;伍忠东(通信作者),教授;火忠彩,硕士研究生。
  • 基金项目:
    甘肃省高等学校创新团队项目(2017C-09);兰州市科技局科技项目(2018-1-51)。

Intrusion Detection Algorithm Based on DBN-KELM

WANG Yang, WU Zhongdong, HUO Zhongcai   

  1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
  • Received:2018-08-06 Revised:2018-10-04 Online:2019-10-15 Published:2019-10-14

摘要: 传统机器学习算法需要人工构建样本特征,处理海量多源异构网络入侵数据时分类效果较差。针对该问题,结合深度信念网络(DBN)和核极限学习机(KELM),提出一种混合深度学习入侵检测算法DBN-KELM。利用DBN提取高维网络历史数据的抽象特征,获得原始数据的低维表示形式。在此基础上,通过KELM对低维表示的数据做监督学习,达到准确识别网络攻击的目的。在NSL-KDD数据集上进行仿真,实验结果表明,DBN-KELM算法能够提高分类准确率,降低对小样本攻击的误报率,同时缩短分类器的训练时间。

关键词: 深度学习, 深度信念网络, 特征提取, 核极限学习机, 入侵检测

Abstract: Traditional machine learning algorithms need to construct sample features manually,which have poor classification effect when dealing with massive multi-source intrusion data in heterogeneous network.To solve this problem,a hybrid deep learning intrusion detection algorithm is proposed combing Deep Belief Network(DBN) with Kernel Extreme Learning Machine(KELM),which is named DBN-KELM.It uses DBN to extract the abstract features of high historical data in dimensional network,so as to obtain the low dimensional representation form of the original data.On this basis,it uses KELM to do supervised learning for low dimensional data to accurately identify the network attack.Simulations are carried out on the NSL-KDD dataset,and the experimental results show that,DBN-KELM algorithm can improve the accuracy of classification,reduce the false alarm rate of small sample attacks and shorten the training time of the classifier.

Key words: deep learning, Deep Belief Network(DBN), feature extraction, Kernel Extreme Learning Machine(KELM), intrusion detection

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