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

页码：122 / +

页数：2

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