A Comparison of Data Mining Techniques for Intrusion Detection

被引：0
|
作者：
Naidu, R. China Appala [1 ]
Avadhani, P. S. [1 ]
机构：
[1] Andhra Univ, Dept CS&SE, Visakhapatnam, Andhra Pradesh, India
来源：
2012 IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES (ICACCCT) | 2012年
关键词：
Intrusion Detection system; Decision Tree; Ripper Rule; Support Vector Machines;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The Expositional increase in the traffic across networks has necessitated the need to detect unauthorized access. In this sense Intrusion Detection has become one of the major research areas In this paper three data mining techniques namely C5.0 Decision Tree, Ripper Rule and Support Vector Machines are studied and compared for the efficiency in detecting the Intrusion, It is found that the C5.0 Decision Tree is efficient than the other two. The data mining tool clementine is used for evaluating this on the KDD99 dataset. The results are also given in this paper.
引用
收藏
页码：41 / 44
页数：4
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