An Ensemble-Based Malware Detection Model Using Minimum Feature Set
Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained u...
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Brno University of Technology
2019-12-01
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| Series: | Mendel |
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| Online Access: | https://mendel-journal.org/index.php/mendel/article/view/102 |
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doaj-c74e8f5ca09f40bb9d0162ca1b83b4462021-07-20T13:20:39ZengBrno University of TechnologyMendel1803-38142571-37012019-12-0125210.13164/mendel.2019.2.001An Ensemble-Based Malware Detection Model Using Minimum Feature SetIvan Zelinka0Eslam AmerTechnical University of Ostrava, Czech Republic Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods. https://mendel-journal.org/index.php/mendel/article/view/102malware detectionmachine learningensemble learning |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Ivan Zelinka Eslam Amer |
| spellingShingle |
Ivan Zelinka Eslam Amer An Ensemble-Based Malware Detection Model Using Minimum Feature Set Mendel malware detection machine learning ensemble learning |
| author_facet |
Ivan Zelinka Eslam Amer |
| author_sort |
Ivan Zelinka |
| title |
An Ensemble-Based Malware Detection Model Using Minimum Feature Set |
| title_short |
An Ensemble-Based Malware Detection Model Using Minimum Feature Set |
| title_full |
An Ensemble-Based Malware Detection Model Using Minimum Feature Set |
| title_fullStr |
An Ensemble-Based Malware Detection Model Using Minimum Feature Set |
| title_full_unstemmed |
An Ensemble-Based Malware Detection Model Using Minimum Feature Set |
| title_sort |
ensemble-based malware detection model using minimum feature set |
| publisher |
Brno University of Technology |
| series |
Mendel |
| issn |
1803-3814 2571-3701 |
| publishDate |
2019-12-01 |
| description |
Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.
|
| topic |
malware detection machine learning ensemble learning |
| url |
https://mendel-journal.org/index.php/mendel/article/view/102 |
| work_keys_str_mv |
AT ivanzelinka anensemblebasedmalwaredetectionmodelusingminimumfeatureset AT eslamamer anensemblebasedmalwaredetectionmodelusingminimumfeatureset AT ivanzelinka ensemblebasedmalwaredetectionmodelusingminimumfeatureset AT eslamamer ensemblebasedmalwaredetectionmodelusingminimumfeatureset |
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