Title: Development of an efficient classifier using proposed sensitivity-based feature selection technique for intrusion detection system

Authors: H.S. Hota; Dinesh K. Sharma; A.K. Shrivas

Addresses: Bilaspur University, Bilaspur (C.G.), India ' Department of Business, Management and Accounting, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA ' Dr. C.V. Raman University, Bilaspur (C.G.), India

Abstract: Intrusion detection system protects an individual computer or network computer from suspicious data and protects the system from unauthorized access. In this paper, we propose a feature selection technique (FST) known as sensitivity based feature selection technique (SBFST) which selects relevant features from intrusion data based on the value of sensitivity. We compare various existing FSTs with the proposed SBFST from three different categories of NSL-KDD data set. Experimental results reveal that C4.5 with SBFST performs better than other existing FSTs and produce a high accuracy of 99.68% with 11 features and 99.95% accuracy with nine features for the multiclass and binary class problems respectively. It has also produced 99.64% accuracy for both multiclass and binary class problems respectively with six and seven features. The performance of proposed SBFST is also verified using the intersection of features, segment by segment with other FSTs and found to be better.

Keywords: feature selection technique; FST; sensitivity-based feature selection technique; SBFST; intrusion detection system; IDS.

DOI: 10.1504/IJICS.2018.089594

International Journal of Information and Computer Security, 2018 Vol.10 No.1, pp.80 - 101

Received: 09 Feb 2016
Accepted: 14 Jan 2017
Published online: 31 Jan 2018 *

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