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Computational Intelligence and Neuroscience
Volume 2012 (2012), Article ID 850259, 10 pages
http://dx.doi.org/10.1155/2012/850259
Research Article

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

Department of Information Science and Technology, Faculty of Information and Communication Engineering, Anna University, Guindy, Chennai 600025, India

Received 14 March 2012; Revised 1 July 2012; Accepted 5 July 2012

Academic Editor: W. J. Chris Zhang

Copyright © 2012 S. Ganapathy 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 systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.