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Hybrid feature selection technique for intrusion detection system

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Kamarudin, Muhammad Hilmi, Maple, Carsten and Watson, Tim (2019) Hybrid feature selection technique for intrusion detection system. International Journal of High Performance Computing and Networking (IJHPCN), 13 (2). pp. 232-240. doi:10.1504/IJHPCN.2019.097503

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Official URL: https://doi.org/10.1504/IJHPCN.2019.097503

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Abstract

High dimensionality’s problems have make feature selection as one of the most important criteria in determining the efficiency of intrusion detection systems. In this study we have selected a hybrid feature selection model that potentially combines the strengths of both the filter and the wrapper selection procedure. The potential hybrid solution is expected to effectively select the optimal set of features in detecting intrusion. The proposed hybrid model was carried out using correlation feature selection (CFS) together with three different search techniques known as best-first, greedy stepwise and genetic algorithm. The wrapper-based subset evaluation uses a random forest (RF) classifier to evaluate each of the features that were first selected by the filter method. The reduced feature selection on both KDD99 and DARPA 1999 dataset was tested using RF algorithm with ten-fold cross-validation in a supervised environment. The experimental result shows that the hybrid feature selections had produced satisfactory outcome.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Intrusion detection systems (Computer security)
Journal or Publication Title: International Journal of High Performance Computing and Networking (IJHPCN)
Publisher: Inderscience
ISSN: 1740-0562
Official Date: 1 February 2019
Dates:
DateEvent
1 February 2019Published
22 January 2019Available
26 August 2016Accepted
Volume: 13
Number: 2
Page Range: pp. 232-240
DOI: 10.1504/IJHPCN.2019.097503
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIED[ERC] Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
Type of Event: Conference
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