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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Embedded Feature Selection Method for a Network-Level Behavioural Analysis Detection Model

Author 1: Mohammad Hafiz Mohd Yusof
Author 2: Mohd Rosmadi Mokhtar
Author 3: Abdullah Mohd. Zain
Author 4: Carsten Maple

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.091271

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 12, 2018.

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Abstract: Feature selection in network-level behavioural analysis studies is used to represent the network datasets of a monitored space. However, recent studies have shown that current behavioural analysis methods at the network-level have several issues. The reduction of millions of instances, disregarded parameters, removed similarities of most of the traffic flows to reduce information noise, insufficient number of optimised features and ignore instances which are not an entity are amongst the other issue that have been identified as the main issues contributing to the inability to predict zero-day attacks. Therefore, this paper aims to select the optimal features that will improve the prediction and behavioural analysis. The training dataset will be trained to use the embedded feature selection method which incorporates both the filter and wrapper method. Correlation coefficient, r and weighted score, wj will be used. The accepted or selected features will be optimised uses Beta distribution functions, β, to find its maximum likelihood, Ɩmax. The final selected features will be trained by the Bayesian Network classifier and tested through several testing datasets. Finally, this method was compared to several other feature selection methods. Final results show the proposed selection method’s performance against other datasets consistently outperform other methods.

Keywords: Feature selection; intrusion detection; behavioural analysis

Mohammad Hafiz Mohd Yusof, Mohd Rosmadi Mokhtar, Abdullah Mohd. Zain and Carsten Maple, “Embedded Feature Selection Method for a Network-Level Behavioural Analysis Detection Model” International Journal of Advanced Computer Science and Applications(IJACSA), 9(12), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091271

@article{Yusof2018,
title = {Embedded Feature Selection Method for a Network-Level Behavioural Analysis Detection Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091271},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091271},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {12},
author = {Mohammad Hafiz Mohd Yusof and Mohd Rosmadi Mokhtar and Abdullah Mohd. Zain and Carsten Maple}
}


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