计算机与现代化Issue(11)：27-30,4.DOI:10.3969/j.issn.1006-2475.2014.11.006
基于模拟退火和半监督聚类的入侵检测方法
Intrusion Detection Based on Simulated Annealing and Semi-supervised Clustering
摘要
Abstract
Because of the absence of supervised data, classical intrusion detection system based on clustering will result in high misdetection rate and low detection rate. In view of this, we propose a method of intrusion detection based on simulated annealing and semi-supervised K-means clustering. This method improves the initial stage of clustering by using a few labeled data of net-work intrusion first, so the semi-supervised learn method is introduced in the K-means clustering. Then the method combines the ability of simulated annealing algorithm jumping out of the local optimal solution with semi-supervised K-means clustering to get global optimal clustering. Finally, the method identifies the clusters with labeled data and is used in the detection of intruding ac-tion. The experiment in the KDDCUP99 data set indicates that the method can improve the clustering algorithm with supervised data and simulated annealing, and obtains an increase in the precision rate of intrusion detection.关键词
入侵检测/半监督K均值聚类/模拟退火Key words
intrusion detection/semi-supervised K-means clustering/simulated annealing分类
信息技术与安全科学引用本文复制引用
吴剑,冯国瑞..基于模拟退火和半监督聚类的入侵检测方法[J].计算机与现代化,2014,(11):27-30,4.基金项目
山东省高等学校科技计划项目(J14LN12) （J14LN12）
山东省高校证据鉴识重点实验室(山东政法学院)开放课题(KFKT (SUPL)-201407) （山东政法学院）
