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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Scientific Publishers
2018
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| Online Access: | https://eprints.ums.edu.my/id/eprint/22278/1/Malware%20Classification%20Using%20Ensemble%20Classifiers.pdf |
| _version_ | 1825713634841460736 |
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| author | Mohd Hanafi Ahmad Hijazi Tan Choon Beng Lim, Yuto Kashif Nisar James Mountstephen |
| author_facet | Mohd Hanafi Ahmad Hijazi Tan Choon Beng Lim, Yuto Kashif Nisar James Mountstephen |
| author_sort | Mohd Hanafi Ahmad Hijazi |
| collection | UMS |
| 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. |
| first_indexed | 2024-03-06T02:59:36Z |
| format | Article |
| id | ums.eprints-22278 |
| institution | Universiti Malaysia Sabah |
| language | English |
| last_indexed | 2024-03-06T02:59:36Z |
| publishDate | 2018 |
| publisher | American Scientific Publishers |
| record_format | dspace |
| spelling | 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 |
| spellingShingle | Mohd Hanafi Ahmad Hijazi Tan Choon Beng Lim, Yuto Kashif Nisar James Mountstephen Malware Classification Using Ensemble Classifiers |
| title | 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_short | Malware Classification Using Ensemble Classifiers |
| title_sort | malware classification using ensemble classifiers |
| url | https://eprints.ums.edu.my/id/eprint/22278/1/Malware%20Classification%20Using%20Ensemble%20Classifiers.pdf |
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