Enhancing Performance of Intrusion Detection through Soft Computing Techniques

Abstract

The worldwide rapid expansion of computer networks and ever growing dependence of organizations on network based information management have led to serious security concerns. Among other security threats network intrusion has been a major concern which requires considerable attention in order to protect the information resources that are accessible via network infrastructure. Though different intrusion detection approaches have been experimented but none of them can guarantee complete protection against network intrusions. Furthering research in this direction, we have been exploring the use of soft computing techniques to analyze intrusion data in order to detect intrusive behavior in network access patters. In this paper, we have carried out some experiments using techniques such as Radial Basis Function Network (RBFN), Self-Organizing Map (SOM), Support Vector Machine (SVM), back propagation, and J48 on the NSL-KDD intrusion data set in order to evaluate the performance of each of the techniques. We have also compared the performance of these techniques with respect to the detection and false alarm rates.