粗糙集属性约简的极限学习机网络入侵检测算法  

ELM network intrusion detection algorithm based on rough set attribute reduction

作　　者：周棒棒 魏书宁[1] 唐勇[2] 马天雨[1] 陈远毅 ZHOU Bang-bang;WEI Shu-ning;TANG Yong;MA Tian-yu;CHEN Yuan-yi(Key Laboratory of Internet of Things Technology and Application,College of Physics and Information Science,Hunan Normal University,Changsha 410006,China;College of Computing,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]湖南师范大学物理与信息科学学院物联网技术及应用重点实验室,湖南长沙410006 [2]国防科学技术大学计算机学院,湖南长沙410073

出　　处：《传感器与微系统》2019年第1期122-125,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(61472437);湖南师大校教改项目(1210786);湖南省教育厅一般项目(531120);湖南师范大学自然科学研究项目(160432)

摘　　要：针对网络入侵数据量大、属性冗余及属性之间线性相关导致分类算法计算速度慢、准确度不高等问题,提出一种改进粗糙集属性约简的极限学习机网络入侵分类算法。对训练集采用粗糙集正域和分辨矩阵相结合的方法获得属性核,筛选出只有属性核的数据集得到无冗余属性的特征集合;使用极限学习机(ELM)作为分类模型进行分类,使用支持向量机(SVM)、神经网络、极限学习机比较证明提出方法的有效性,为网络入侵检测提供一种新的解决方法。Aiming at problem of slow computing speed and inaccuracy of the classification algorithm caused by large numbers of network intrusions,attribute redundancy and linear correlation between attributes,an extreme learning machine(ELM)network intrusion classification algorithm is proposed which is based on reduction of redundant attributes by the positive domain and discernibility matrixof the rough set.After the reduction of redundant attributes by the positive domain and discernibility matrix of the rough set,it gets the characteristic collection of non-redundant attributes.ELM serves as classification model.Through comparing it with SVM,neural network,ELM,the effectiveness of this method is proved,thus provide a new solution for network intrusion detection.

关 键 词：数据冗余 粗糙集正域 粗糙集分辨矩阵 极限学习机 入侵检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]