Intelligent agent-based intrusion detection system using enhanced multiclass SVM

Authors:
S. Ganapathy;P. Yogesh;A. Kannan
Affiliations:
Department of Information Science and Technology, Anna University, Guindy, Chennai, India;Department of Information Science and Technology, Anna University, Guindy, Chennai, India;Department of Information Science and Technology, Anna University, Guindy, Chennai, India
Venue:
Computational Intelligence and Neuroscience
Year:
2012

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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 alarmrate. 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 EnhancedMulticlass Support VectorMachine 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.