Intrusion Patterns Recognition in Computer Networks

被引：0

作者：

Farzan, Ali ^{[1
,2
]}

Razavi, Naser ^{[1
]}

Balafar, Mohammad Ali ^{[1
]}

Arvin, Farshad ^{[1
]}

机构：

[1] Islamic Azad Univ, Shabestar Branch, Shabestar, Iran

[2] Univ Putra Malaysia, Serdang 43400, Malaysia

来源：

WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, WCECS 2011, VOL I | 2011年

关键词：

k-mean; Bayesian; SVM; Network Intrusion Detection;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

One of the hottest research areas in recent years is detecting network intrusion patterns in computer networks. Because of dynamic nature of intrusion patterns in networks, intelligently inspecting the behavior of networks and detecting anomalies are mostly desirable. KDD-Cup99 pattern database are used as a standard source of network packets in our research. K-mean, Bayesian method and Support Network Machine (SVM) are used as anomaly detectors. Results show the superiority of SVM over other two methods regarding the accuracy of classifying patterns into normal packets and suspicious ones. It can be concluded that using high dimensional pattern recognition methods have reasonable competence in detecting attack patterns in computer networks.

引用

页码：433 / 436

页数：4

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