K-means聚类算法在入侵检测中的应用  

Application of K-means Clustering Algorithm in Intrusion Detection

作　　者：李洋[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410076

出　　处：《计算机工程》2007年第14期154-156,共3页Computer Engineering

摘　　要：提出了一种基于聚类分析方法构建入侵检测库的模型,实现了按K-平均值方法建立入侵检测库并据此划分安全等级的思想。该检测系统的建立不依赖于经验数据,能自动依据原有数据对入侵行为进行重新划分。仿真实验表明,该方法具有较强的实用性和自适应功能。This paper introduces an intrusion detection model based on clustering analysis and realizes an algorithm of K-means which can set up a database of intrusion detection and classify safe levels. Experiential data are not required to set up this detection system, which is capable of re-classifying intrusion behaviors in terms of related data automatically. Simulation experiments show that the technique possesses strong applicability and self-adaptability.

关 键 词：网络安全 入侵检测 数据挖掘 聚类分析 K-平均值 

分 类 号：TP309.1[自动化与计算机技术—计算机系统结构]