Network Intrusion Detection by Support Vectors and Ant Colony

被引：0

作者：

Zhang, Qinglei ^{[1
]}

Feng, Wenying ^{[1
]}

机构：

[1] McMaster Univ, Fac Engn, Dept Comp & Software, Hamilton, ON L8S 4L8, Canada

来源：

PROCEEDINGS OF 2009 INTERNATIONAL WORKSHOP ON INFORMATION SECURITY AND APPLICATION | 2009年

关键词：

Network security; network attack; Intrusion Detection Systems (IDS); Support Vector Machine (SVM); Ant Colony Network;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a framework for a new approach in intrusion detection by combining two existing machine learning methods (i.e. SVM and CSOACN). The IDS based on the new algorithm can be applied as pure SVM, pure CSOACN or their combination by constructing the detection classifier under three different training modes respectively. The initial experiments indicate that performance of their combination is better than pure SVM in terms of higher average detection rate as well as lower rates of both negative and positive false and is better than pure CSOACN in terms of less training time with comparable detection rate and false alarm rates.

引用

页码：639 / 642

页数：4

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