Intrusion detection using an ensemble of intelligent paradigms

Authors:
Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham
Affiliations:
Department of Computer Science, New Mexico Tech, Socorro, NM;Department of Computer Science, New Mexico Tech, Socorro, NM;Department of Computer Science, Oklahoma State University, Tulsa, OK
Venue:
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Year:
2005

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Abstract

Soft computing techniques are increasingly being used for problem solving. This paper addresses using an ensemble approach of different soft computing and hard computing techniques for intrusion detection. Due to increasing incidents of cyber attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. We studied the performance of Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Multivariate Adaptive Regression Splines (MARS). We show that an ensemble of ANNs, SVMs and MARS is superior to individual approaches for intrusion detection in terms of classification accuracy.