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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578548
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dc.contributor.authorWael Farouk Elsersy (UM)
dc.contributor.authorNor Badrul Anuar (UM)
dc.date.accessioned2023-11-06T03:03:27Z-
dc.date.available2023-11-06T03:03:27Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116013
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578548-
dc.descriptionOver the last few years, the Android smartphone had faced attacks from malware and malware variants, as there is no effective commercial Android security framework in the market. Thus, using machine learning algorithms to detect Android malware applications that can fit with the smartphone resources limitations became popular. This paper used state of the art Deep Belief Network in Android malware detection. The Lasso is one of the best interpretable ?1-regularisation techniques which proved to be an efficient feature selection embedded in learning algorithm. The selected features subset of Restricted Boltzmann Machines tuned by Harmony Search feature reduction with Deep Belief Network classifier was used, achieving 85.22% Android malware detection accuracy.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-S-6
dc.rightsUKM
dc.subjectAndroid malware detection
dc.subjectDeep belief network
dc.subjectFeature learning
dc.subjectMachine learning algorithms
dc.titleAndroid malware detection using deep belief network
dc.typeJournal Article
dc.format.volume25
dc.format.pages143-150
dc.format.issueSpecial Issue
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