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dc.contributor.authorYerima, Suleimanen
dc.contributor.authorSezer, Sakiren
dc.contributor.authorMcWilliams, G.en
dc.date.accessioned2018-10-31T10:38:09Z
dc.date.available2018-10-31T10:38:09Z
dc.date.issued2013-12-19
dc.identifier.citationYerima, S. Y., Sezer, S. and McWilliams, G. (2014) Analysis of Bayesian classification-based approaches for Android malware detection. IET Information Security, 8(1), pp. 25-36.en
dc.identifier.urihttp://digital-library.theiet.org/content/journals/10.1049/iet-ifs.2013.0095
dc.identifier.urihttp://hdl.handle.net/2086/16931
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractMobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.en
dc.language.isoenen
dc.subjectmalware detectionen
dc.subjectmobile malwareen
dc.subjectData securityen
dc.subjectinvasive softwareen
dc.subjectmachine learningen
dc.subjectKnowledge engineeringen
dc.subjectsmart phone securityen
dc.subjectAndroid malwareen
dc.titleAnalysis of Bayesian classification-based approaches for Android malware detectionen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1049/iet-ifs.2013.0095
dc.researchgroupCyber Technology Institute (CTI)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2013-10-07en
dc.researchinstituteCyber Technology Institute (CTI)en


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