A Self-Organizing Map and its Modeling for Discovering Malignant Network Traffic

被引:0
作者
Langin, Chet [1 ]
Zhou, Hongbo [1 ]
Rahimi, Shahram [1 ]
Gupta, Bidyut [1 ]
Zargham, Mehdi [1 ]
Sayeh, Mohammad R. [2 ]
机构
[1] So Illinois Univ, Dept Comp Sci, Carbondale, IL 62901 USA
[2] Southern Illinois Univ, Dept Elect & Comp Engn, Carbondale, IL 62901 USA
来源
IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN CYBER SECURITY | 2009年
关键词
ANOMALY DETECTION;
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
Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P botnet traffic and other malignant network activity by using a Self-Organizing Map (SOM) self-trained on denied Internet firewall log entries. The SOM analyzed new firewall log entries in a case study to classify similar network activity, and discovered previously unknown local P2P bot traffic and other security issues.
引用
收藏
页码:122 / +
页数:2
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