Intrusion detection model based on deep belief nets  

基于深度信念网络的入侵检测模型（英文）

作　　者：高妮[1] 高岭[1] 贺毅岳[1,2] 高全力[1] 任杰[1] 

机构地区：[1]西北大学信息科学与技术学院,西安710127 [2]西北大学经济管理学院,西安710127

出　　处：《Journal of Southeast University(English Edition)》2015年第3期339-346,共8页东南大学学报（英文版）

基　　金：The National Key Technology R&D Program during the 12th Five-Year Plan Period(No.2013BAK01B02);the National Natural Science Foundation of China(No.61373176);the Scientific Research Projects of Shaanxi Educational Committee(No.14JK1693)

摘　　要：This paper focuses on the intrusion classification of huge amounts of data in a network intrusion detection system. An intrusion detection model based on deep belief nets （DBN） is proposed to conduct intrusion detection,and the principles regarding DBN are discussed.The DBN is composed of a multiple unsupervised restricted Boltzmann machine （RBM） and a supervised back propagation （BP）network.First,the DBN in the proposed model is pre-trained in a fast and greedy way,and each RBM is trained by the contrastive divergence algorithm.Secondly,the whole network is fine-tuned by the supervised BP algorithm,which is employed for classifying the low-dimensional features of the intrusion data generated by the last RBM layer simultaneously.The experimental results on the KDD CUP 1999 dataset demonstrate that the DBN using the RBM network with three or more layers outperforms the self-organizing maps （SOM）and neural network （NN）in intrusion classification.Therefore,the DBN is an efficient approach for intrusion detection in high-dimensional space.研究了入侵检测系统中海量数据分类的问题.讨论了深度信念网络(DBN)的原理,提出了基于DBN的入侵检测模型.DBN由多层无监督的限制玻尔兹曼机(RBM)网络和一层有监督的反向传播(BP)网络构成.该入侵检测模型采用一种快速、贪婪的方法对DBN网络进行预训练,利用对比分歧算法逐层训练每一个RBM网络;然后,利用有监督的BP算法对整个DBN网络进行微调,并同时对RBM网络输出的低维特征进行入侵数据分类.基于KDD CUP 1999数据集的实验结果表明,使用3层以上的DBN模型分类效果优于自组织映射和神经网络方法.因此,DBN是一种有效且适用于高维特征空间的入侵检测方法.

关 键 词：intrusion detection deep belief nets restricted Boltzmann machine deep learning 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]