Intrusion detection using Emergent Self-Organizing Maps

被引：0
|
作者：
Mitrokotsa, Aikaterini [1 ]
Douligeris, Christos [1 ]
机构：
[1] Univ Piraeus, Dept Informat Cs, Piraeus 18534, Greece
来源：
ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2006年 / 3955卷
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：
In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) based on Kohonen Self -Organizing maps in order to detect intrusive behaviours. The)proposed approach combines machine learning and information visualization techniques to analyze network traffic and is based on classifying "normal" versus "abnormal" traffic. The results are promising as they show the. ability of eSOMs to classify normal against abnormal behaviour regarding false alarms and detection probabilities.
引用
收藏
页码：559 / 562
页数：4
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