An Ensemble-Based Malware Detection Model Using Minimum Feature Set
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Date
2019-12-20
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Mark
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Institute of Automation and Computer Science, Brno University of Technology
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Abstract
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.
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Citation
Mendel. 2019 vol. 25, č. 2, s. 1-10. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/102
https://mendel-journal.org/index.php/mendel/article/view/102
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Peer-reviewed
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en
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Defence
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0
http://creativecommons.org/licenses/by-nc-sa/4.0