摘要
运用数据挖掘的方法进行入侵检测已经成为网络安全领域的一个热点研究方向,该文主要对异常检测进行研究,将一种快速DBSCAN聚类算法应用到入侵检测中,通过对数据进行聚类,从而发现其中未知的攻击行为。该文以KDD99数据集为例做实验,证明了DBSCAN算法具有很好的聚类效果,实验结果得到了较高的检测率和较低的误报率。
To detect intrusion with the method of data miningg has been a hot research direction in the field of network security. The faster DBSCAN cluster algorithm Which is used in mainly discussed in this paper,can detect the unknown attacks, by clustering the data.The paper shows this by carrying out experiment on KDD99 data set,whose result achieves higher attack detection rate with tower false positive rate.
出处
《计算机安全》
2007年第8期43-46,共4页
Network & Computer Security
关键词
入侵检测
异常
聚类
DBSCAN
Intrusion Detection
Anomaly Detection
Clustering
Dbscan
