计算机应用与软件2024，Vol.41Issue(6)：62-71,10.DOI:10.3969/j.issn.1000-386x.2024.06.010
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
摘要
Abstract
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Key words
Intrusion detection/Deep learning/Deep belief network/Locality preserving projection分类
信息技术与安全科学引用本文复制引用
武玉坤,李伟,陈沅涛..深度置信网络融合局部保持投影的入侵检测模型[J].计算机应用与软件,2024,41(6):62-71,10.基金项目
国家自然科学基金项目(61502422,61972056) （61502422,61972056）
浙江省自然科学基金项目(LY18F020028) （LY18F020028）
浙江省科技厅公益项目(2017C33108) （2017C33108）
浙江省教育厅一般科研项目(Y202044619) （Y202044619）
杭州职业技术学院高层次人才科研启动项目(RCXY202243). （RCXY202243）
