Title: Feature subset selection for intrusion detection using various rank-based algorithms

Authors: K. Keerthi Vasan; B. Surendiran

Addresses: National Institute of Technology Puducherry, Karaikal, India ' National Institute of Technology Puducherry, Karaikal, India

Abstract: Feature selection in data mining enables the identification of significant features constituting the given data. It facilitates identification and isolation of profitable features to ensure quality in the underlying information. Feature selection achieves dimension reduction of data making mining tasks less complex. Ranker-based feature selection algorithms evaluate the features and generate a rank-list based on their score using which desirable features are identified to generate a subset. The various ranker algorithms include Relief-F, Information Gain, Gini Index, Correlation, and Minimum Redundancy Maximum Relevance. In this work, rankers have been used to perform feature selection for intrusion detection. Experiments have been carried out on the SSE Net 2011 data set and a machine learning classifier determines the accuracy of classification. Accuracy plots are generated and the threshold on the number of features to be selected is decided and substantial features in the data set are identified.

Keywords: feature selection; ranker algorithm; SSE Net 2011 data set; feature rank-list; accuracy plot; feature threshold; C/V ratio; filter and wrapper methods.

DOI: 10.1504/IJCAT.2017.086017

International Journal of Computer Applications in Technology, 2017 Vol.55 No.4, pp.298 - 307

Received: 16 Jun 2016
Accepted: 05 Sep 2016
Published online: 21 Aug 2017 *

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