Feature subset selection in large dimensionality domains
Pattern Recognition
Features selection for intrusion detection systems based on support vector machines
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
Features selection approaches for intrusion detection systems based on evolution algorithms
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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Existing intrusion detection techniques emphasize on building intrusion detection model based on all features provided. But all features are not relevant and some of them are redundant and useless. In this paper, we propose and investigate a fast hybrid feature selection method - a fusion of Correlation-based Feature Selection, Support Vector Machine and Genetic Algorithm - to determine an optimal feature set. An appropriate feature set helps to build efficient decision model as well as reduced feature set lights up the training and testing process considerably. We have examined the feasibility of our approach by conducting several experiments using KDD 1999 CUP intrusion dataset. Experimental results indicate the reduction of training and testing time by an order of magnitude while maintaining the detection accuracy within tolerable range.