基于K-Means聚类算法入侵检测系统研究

Research on Intrusion Detection Based on K-means algorithms Clusting Algorithm

网络安全是网络研究的热点,而随着对计算机系统弱点和入侵行为分析研究的深入,入侵检测系统在网络安全中发挥着越来越重要的作用,并成为处理网络安全问题的有效工具。提出的许多聚类算法及其变种在增量式聚类算法研究方面所做工作较少的问题。通过对K-Means聚类算法、迭代算法的改进,提出优化算法。很好地解决传统聚类算法在伸缩性、数据定期更新上所面临的问题。基于K-Means聚类算法入侵检测系统中重要的数据集常用的数据分析方法,搭建检测系统发现入侵行为。

The network security is becoming a hot area in network researches. With the comprehensive analysis of the vulnerabili- ty of the network and intrusion behaviors, the Intrusion Detection System （IDS） becomes more and more important in network security. IDS is an important supplement to the traditional network security technologies. When updates are collected and ap- plied to the databases ,then,all patterns derived from the databases by K-means algorithms have to be updated as well. Due to the very large size of the databases,it is highly desirable to perform these updates incrementally. The commonly-used Tec logical means of data analysis and the development trend of the intrusion detection technology.Experimental results show that the algo- rithms proposed in this paper are efficient, and the anticipated results are realized.