计算机与数字工程2016，Vol.44Issue(9)：1770-1773,4.DOI:10.3969/j.issn.1672-9722.2016.09.031
基于改进 SVM 主动学习的网络入侵检测磁
An Improved Incremental Bayes Classificaiton Model
苏志同 1刘芳正1
作者信息
- 1. 北方工业大学计算机学院 北京 100144
- 折叠
摘要
Abstract
Support vector machine(SVM ) active learning model can solve the problem of small sample learning of intru‐sion detection system ,and improve the performance of the classifier in intrusion detection system .For SVM active learngin model to construct the initial training set is random ,the nuclear spatial clustering of the initial training set construction meth‐od were optimized ,and the introduction of ant colony clustering algorithm reducing sample selection rules on the classification performance effect .The results show that the improved model can effectively improve the intrusion detection system of clas ‐sification performance .关键词
入侵检测/支持向量机/主动学习/分类性能Key words
intrusion detection/support vector machine/active learning/classification performance Class Number TP393 .08分类
信息技术与安全科学引用本文复制引用
苏志同,刘芳正..基于改进 SVM 主动学习的网络入侵检测磁[J].计算机与数字工程,2016,44(9):1770-1773,4.
