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Malware Classification Using Ensemble Classifiers

Antimalware offers detection mechanism to detect and take appropriate action against malware detected. To evade detection, malware authors had introduced polymorphism to malware. In order to be effectively analyzing and classifying large amount of malware, it is necessary to group and identify them...

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Main Authors: Mohd Hanafi Ahmad Hijazi, Tan Choon Beng, Lim, Yuto, Kashif Nisar, James Mountstephen
Format: Article
Language:English
Published: American Scientific Publishers 2018
Online Access:https://eprints.ums.edu.my/id/eprint/22278/1/Malware%20Classification%20Using%20Ensemble%20Classifiers.pdf
https://eprints.ums.edu.my/id/eprint/22278/
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spelling my.ums.eprints.222782019-06-18T08:26:27Z https://eprints.ums.edu.my/id/eprint/22278/ Malware Classification Using Ensemble Classifiers Mohd Hanafi Ahmad Hijazi Tan Choon Beng Lim, Yuto Kashif Nisar James Mountstephen Antimalware offers detection mechanism to detect and take appropriate action against malware detected. To evade detection, malware authors had introduced polymorphism to malware. In order to be effectively analyzing and classifying large amount of malware, it is necessary to group and identify them into their corresponding families. Hence, malware classification has appeared as a need in securing our computer systems. Algorithms and classifiers such as k-Nearest Neighbor, Artificial Neural Network, Support Vector Machine, Naïve Bayes, and Decision Tree had shown their effectiveness towards malware classification in various recent researches. This paper proposed the concept of ensemble classifications to classify malwares, in which three individual classifiers, k-Nearest Neighbor, Decision Tree and Naïve Bayes classifiers are ensemble by using the bagging approach. American Scientific Publishers 2018 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/22278/1/Malware%20Classification%20Using%20Ensemble%20Classifiers.pdf Mohd Hanafi Ahmad Hijazi and Tan Choon Beng and Lim, Yuto and Kashif Nisar and James Mountstephen (2018) Malware Classification Using Ensemble Classifiers. Advanced Science Letters, 24 (2). pp. 1172-1176. ISSN 1936-6612 DOI: https://doi.org/10.1166/asl.2018.10710
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
description Antimalware offers detection mechanism to detect and take appropriate action against malware detected. To evade detection, malware authors had introduced polymorphism to malware. In order to be effectively analyzing and classifying large amount of malware, it is necessary to group and identify them into their corresponding families. Hence, malware classification has appeared as a need in securing our computer systems. Algorithms and classifiers such as k-Nearest Neighbor, Artificial Neural Network, Support Vector Machine, Naïve Bayes, and Decision Tree had shown their effectiveness towards malware classification in various recent researches. This paper proposed the concept of ensemble classifications to classify malwares, in which three individual classifiers, k-Nearest Neighbor, Decision Tree and Naïve Bayes classifiers are ensemble by using the bagging approach.
format Article
author Mohd Hanafi Ahmad Hijazi
Tan Choon Beng
Lim, Yuto
Kashif Nisar
James Mountstephen
spellingShingle Mohd Hanafi Ahmad Hijazi
Tan Choon Beng
Lim, Yuto
Kashif Nisar
James Mountstephen
Malware Classification Using Ensemble Classifiers
author_facet Mohd Hanafi Ahmad Hijazi
Tan Choon Beng
Lim, Yuto
Kashif Nisar
James Mountstephen
author_sort Mohd Hanafi Ahmad Hijazi
title Malware Classification Using Ensemble Classifiers
title_short Malware Classification Using Ensemble Classifiers
title_full Malware Classification Using Ensemble Classifiers
title_fullStr Malware Classification Using Ensemble Classifiers
title_full_unstemmed Malware Classification Using Ensemble Classifiers
title_sort malware classification using ensemble classifiers
publisher American Scientific Publishers
publishDate 2018
url https://eprints.ums.edu.my/id/eprint/22278/1/Malware%20Classification%20Using%20Ensemble%20Classifiers.pdf
https://eprints.ums.edu.my/id/eprint/22278/
_version_ 1760229947892301824
score 13.211869

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