基于数据挖掘的增量密度聚类异常检测算法
nomaly detection algorithm using Incremental density clustering based on data mining
本文介绍了一种基于数据挖掘技术检测入侵检测模型的方法。把基于密度聚类的算法应用于入侵检测系统中，并且利用增量算法来解决静态聚类形成的轮廓不容易改变的缺点，动态的调整正常行为轮廓。以聚类为代表的无监督异常检测方法可以在无标记数据集上使用，比传统的数据挖掘方法具有一定的应用优势。
his paper introduces a method based on data mining technology detection method of intrusion detection model. The density of clustering algorithm is applied to the intrusion detection system, and using the increment algorithm to solve the fault that the static clustering form outline is not easy change,then adjust the profiles of normal behavior dynamicly.As a representative of the clustering without supervision anomaly detection methods can be used without mark dataset than the traditional data mining method has advantages of application.
李文龙、乔佩利
计算技术、计算机技术
数据挖掘聚类增量入侵检测
data miningclusteringincrementalintrusion detection
李文龙,乔佩利.基于数据挖掘的增量密度聚类异常检测算法[EB/OL].(2010-12-30)[2025-08-16].http://www.paper.edu.cn/releasepaper/content/201012-1351.点此复制
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