南京理工大学学报（自然科学版）2017，Vol.41Issue(4)：420-427,8.DOI:10.14177/j.cnki.32-1397n.2017.41.04.004
基于粗糙加权平均单依赖估计的入侵检测算法
Intrusion detection algorithm based on rough weightily averaged one-dependence estimators
摘要
Abstract
Intrusion detection,as an important direction of network security,is gaining more and more attentions.A large number of traditional data mining algorithms are applied to the data analysis field of intrusion detection.With the increasing of network bandwidth,the great increasing amount of data and the various kinds of protocol types make the applications of these traditional algorithms encounter many reality problems,such as poor accuracy,low operating efficiency,difficulties of parameter selection,etc.In this paper,we propose an intrusion detection algorithm called rough weightily averaged one-dependence estimator,which is based on the rough set theory and Bayesian theory.This algorithm uses a subtraction method based on the rough set theory to reduce the attributes of network data,and uses weightily averaged one-dependence estimators to classify the data.By combining these two methods,this algorithm can do intrusion detection with low resource consumption and easy implementation.Experiment shows that the algorithm has better operating efficiency and accuracy compared with traditional algorithms.关键词
入侵检测/粗糙集理论/属性约减/贝叶斯理论/粗糙加权平均单依赖估计Key words
intrusion detection/rough set theory/attribute reduction/Bayesian theory/rough weightily averaged one-dependence estimators分类
信息技术与安全科学引用本文复制引用
耿夏琛,李千目,叶德忠,巫忠正,蒋勇..基于粗糙加权平均单依赖估计的入侵检测算法[J].南京理工大学学报（自然科学版）,2017,41(4):420-427,8.基金项目
国家重点研发计划政府间国际科技创新合作重点专项(S2016G9070) （S2016G9070）
江苏省重大研发计划社会发展项目(BE2017739) （BE2017739）
江苏省重大研发计划产业前瞻项目(BE2017100) （BE2017100）
中央高校基本科研业务费专项资金(30916015104) （30916015104）
赛尔下一代互联网创新项目(NGII20160122) （NGII20160122）
中兴通讯产学研合作论坛合作项目(2016ZTE04-11) （2016ZTE04-11）
