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.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第14期154-156,共3页
Computer Engineering
关键词
网络安全
入侵检测
数据挖掘
聚类分析
K-平均值
network security
intrusion detection
data mining
clustering analysis
K-means
