University of Bahrain
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Efficient algorithm for malware classification: n-gram MCSC

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dc.contributor.author Lim, Myung-Jae
dc.contributor.author Kwon, Young-Man
dc.date.accessioned 2020-03-01T01:07:58Z
dc.date.available 2020-03-01T01:07:58Z
dc.date.issued 2020-03-01
dc.identifier.issn 2210-142X
dc.identifier.uri https://journal.uob.edu.bh:443/handle/123456789/3788
dc.description.abstract In this paper, we proposed n-gram MCSC. This method extracts n-gram opcode from execution file and use Simhash to make image of them. We measured and compared the performance metrics of n-gram MCSC and existing MCSC such as accuracy, loss, precision and AUC value of PR curve and ROC curve. To verify whether the difference of accuracy is significant statistically or not, we made experiments of it thirty times and did the ANOVA analysis. We found it was significant. As the result of post-hoc analysis, n-gram MCSC showed better result than existing MCSC in accuracy. The 2-gram MCSC showed the better result than 3-gram MCSC in terms of accuracy, precision, the AUC value of PR curve and ROC curve. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Malware classification, Malware detection, Malware visualization, Simhash, N-gram, Deep learning en_US
dc.title Efficient algorithm for malware classification: n-gram MCSC en_US
dc.identifier.doi http://dx.doi.org/10.12785/ijcds/090204
dc.volume 9 en_US
dc.issue 2 en_US
dc.pagestart 179 en_US
dc.pageend 185 en_US
dc.contributor.authorcountry Korea (South) en_US
dc.contributor.authoraffiliation Eulji University en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US


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