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

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.