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Proceedings of 2009 International Workshop on Information Security and Application (IWISA 2009)

Qingdao, China, November 21-22, 2009

Editors: Feng Gao and Xijun Zhu

AP Catalog Number: AP-PROC-CS-09CN004

ISBN: 978-952-5726-06-0

Page(s): 639-642

Network Intrusion Detection by Support Vectors and Ant Colony

Qinglei Zhang and Wenying Feng

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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.

Index Terms

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

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