A classified method based on support vector machine for grid computing intrusion detection

被引：0
|
作者：
Zheng, QH [1 ]
Li, H [1 ]
Xiao, Y [1 ]
机构：
[1] Xian Jiaotong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China
来源：
GRID AND COOPERATIVE COMPUTING GCC 2004, PROCEEDINGS | 2004年 / 3251卷
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A novel ID method based on Support Vector Machine (SVM) is proposed to solve the classification problem for the large amount of raw intrusion event dataset of the grid computing environment. A new radial basic function (RBF), based on heterogeneous value difference metric (HVDM) of heterogeneous datasets, is developed. Two different types of SVM, Supervised C_SVM and unsupervised One_Class SVM algorithms with kernel function, are applied to detect the anomaly network connection records. The experimental results of our method on the corpus of data collected by Lincoln Labs at MIT for an intrusion detection system evaluation sponsored by the U.S. Defense Advanced Research Projects Agency (DARPA) shows that the proposed method is feasible and effective.
引用
收藏
页码：875 / 878
页数：4
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