计算机工程与应用Issue(17)：124-127,144,5.DOI:10.3778/j.issn.1002-8331.1309-0161
改进K-means算法在入侵检测中的应用研究
Application research of improved K-means algorithm in intrusion detection
摘要
Abstract
An improved K-means clustering algorithm is put forward on basis of the split-merge method for the purpose of remedying defects both in determination of value in K and in selection of initial cluster centre of traditional K-means clustering. The concept of independence degree of date is incorporated into the experimental date subset construction theory, using independence degree to evaluate the importance of nature. The database is merged into several classes in respect of density of date points, the combination of the minimum spanning tree algorithm and traditional K-means clustering algo-rithm is conducive to the achievement of splitting. The KDD Cup99 database is applied to conduct simulation experiment on the application of the improved algorithm in intrusion detection. The results indicate that the improved algorithm pre-vails over traditional K-means algorithm in detection rate and false alarm rate.关键词
入侵检测/数据挖掘/聚类算法/K-means聚类/最小支撑树Key words
intrusion detection/data mining/clustering algorithm/K-means clustering/minimum spanning tree分类
信息技术与安全科学引用本文复制引用
王茜,刘胜会..改进K-means算法在入侵检测中的应用研究[J].计算机工程与应用,2015,(17):124-127,144,5.基金项目
科技部国家科技支撑计划重点项目（No.2011BAH25B04）。 （）
