Attack characterization and intrusion detection using an ensemble of Self-Organizing Maps

被引：0
|
作者：
DeLooze, Lori L.
机构：
来源：
2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 | 2006年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Self-Organized Maps (SOM) use an unsupervised learning technique to independently organize a set of input patterns into various classes. In this paper, we use an ensemble of SOMs to identify computer attacks and characterize them appropriately using the major classes of computer attacks (Denial of Service, Probe, User-to-Root and Remote-to-Local). The procedure produces a set of confidence levels for each connection as a way to describe the connection's behavior.
引用
收藏
页码：2121 / 2128
页数：8
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