邻域模型在入侵检测中的应用

Application of Neighborhood Model in Intrusion Detection

根据对象邻域的分离度和耦合度确定初始聚类中心,提出一种基于邻域模型的k-means改进算法,并以KDD CUP 99数据集为对象,对入侵检测进行了仿真实验.结果显示,改进后的算法在入侵检测率和误检率方面均优于IKCM算法和传统的k-means算法.

This paper constructively advances the k-means,an improved algorithm based on neighbor-hood model by ascertaining the initial clustering centre according to the degree of separation and coupling in object neighborhood.It further carries the simulation experiment on intrusion detection system with the KDD CUP 99 data sets as the experimental subjects.It strongly supports the conclusion that the improved algorithm is superior to IKCM algorithm and the traditional k-means algorithm either in intrusion detection rate and false detection rate.