深度置信网络融合局部保持投影的入侵检测模型  

INTRUSION DETECTION MODEL BASED ON DEEP BELIEF NETWORK FUSING LOCALITY PRESERVING PROJECTION

作　　者：武玉坤 李伟[2] 陈沅涛[3] Wu Yukun;Li Wei;Chen Yuantao(Hangzhou Vocational and Technical College,Hangzhou 310018,Zhejiang,China;College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,Zhejiang,China;School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410114,Hunan,China)

机构地区：[1]杭州职业技术学院,浙江杭州310018 [2]浙江工业大学计算机科学与技术学院,浙江杭州310023 [3]长沙理工大学计算机与通信工程学院,湖南长沙410114

出　　处：《计算机应用与软件》2024年第6期62-71,共10页Computer Applications and Software

基　　金：国家自然科学基金项目(61502422,61972056);浙江省自然科学基金项目(LY18F020028);浙江省科技厅公益项目(2017C33108);浙江省教育厅一般科研项目(Y202044619);杭州职业技术学院高层次人才科研启动项目(RCXY202243)。

摘　　要：网络入侵检测系统(NIDS)提供了比其他传统网络防御技术(如防火墙系统)更好的网络安全解决方案。提出一种深度置信网络(DBN)与局部保持投影技术相融合的入侵检测模型。深度置信网络用于原始数据的特征学习;采用局部保持投影(LPP)融合深层特征,进一步去除冗余和无关特征。最后使用Softmax分类器进行分类。研究该方法在NSL-KDD数据集和UNSW-NB15数据集上的准确率、检测率、误报率等分类指标,并与常规的机器学习分类方法及其他文献中最新的方法进行比较。实验结果表明DBN-LPP模型提高了入侵检测的综合性能,其性能优于传统的机器学习分类方法及其他方法,为入侵检测提供了一种新的研究方法。Network intrusion detection systems(NIDS)provide a better solution to network security than other traditional network defense technologies,such as firewall systems.This paper proposes an intrusion detection model that combines deep belief network(DBN)and local preserving projection(LPP).The DBN was used for feature learning of the original data,and the LPP was used to fuse the deep features to further remove redundant and irrelevant features.Softmax classifier was used for classification.In addition,the accuracy,detection rate,false alarm rate and other classification indicators of this method on the NSL-KDD data set and UNSW-NB15 data set were studied and compared with the conventional machine learning classification method and the latest model method in other literature.The experimental results show that the DBN-LPP model improves the comprehensive performance of intrusion detection system,and its performance is better than traditional machine learning classification methods and other methods.This paper provides a new research method for intrusion detection.

关 键 词：入侵检测 深度学习 深度置信网络 局部保持投影 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]