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The Scientific World Journal
Volume 2015, Article ID 574589, 15 pages
http://dx.doi.org/10.1155/2015/574589
Research Article

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

1Department of CSE, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore 641 108, India
2Department of CSE, SNS College of Technology, Coimbatore 641 035, India

Received 20 January 2015; Revised 19 May 2015; Accepted 31 May 2015

Academic Editor: Giuseppe A. Trunfio

Copyright © 2015 P. Amudha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.