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dc.contributor.authorYerima, Suleimanen
dc.contributor.authorSezer, Sakiren
dc.contributor.authorMuttik, I.en
dc.date.accessioned2018-10-31T10:42:38Z
dc.date.available2018-10-31T10:42:38Z
dc.date.issued2015-10-12
dc.identifier.citationYerima, S. Y., Sezer, S., Muttik, I. (2015) High accuracy android malware detection using ensemble learning. IET Information Security, 9(6), pp. 313-320.en
dc.identifier.urihttp://digital-library.theiet.org/content/journals/10.1049/iet-ifs.2014.0099
dc.identifier.urihttp://hdl.handle.net/2086/16932
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.abstractWith over 50 billion downloads and more than 1.3 million apps in Google's official market, Android has continued to gain popularity among smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature-based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus, this study proposes an approach that utilises ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3–99% detection accuracy with very low false positive rates.en
dc.language.isoenen
dc.publisherThe Institution of Engineering and Technologyen
dc.subjectAndroid Malwareen
dc.subjectMalware Detectionen
dc.subjectEnsemble Learningen
dc.subjectData Securityen
dc.subjectMachine Learningen
dc.subjectSmart phone securityen
dc.titleHigh Accuracy Android Malware Detection Using Ensemble Learningen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1049/iet-ifs.2014.0099
dc.researchgroupCyber Technology Institute (CTI)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2015-02-06en
dc.researchinstituteCyber Technology Institute (CTI)en
dc.exception.ref2021codes254aen


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