Abstract: Redundant and irrelevant features in data have caused a long -term problem in network traffic classification. These features not only slow down the process of classification but also prevent the classifier from making accurate decisions, especially while copying the big data. In this paper we propose a mutual information based algorithm that analytically selects the optimal feature for classification. The mutual information based feature selection can handle both linear and non linear dependent data features. An Intrusion Detection Systems (IDS) named Least Square Vector Machine Based IDS(LSSVM-IDS),is build using the features selected by our proposed feature selection algorithms.
Keywords: Intrusion detection, feature selection, mutual information, linear correlation coefficient, least square support vector machine.
Title: Building an Intrusion Detection System Using Filter-Based Feature Selection Algorithm
Author: Mrs. Shilpa S G, Vivek YS, Suhas B V, Veerendra Kumar C G
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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