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dc.contributor.authorYerima, Suleimanen
dc.contributor.authorSezer, Sakiren
dc.contributor.authorKang, B.en
dc.contributor.authorMcLaughlin, K.en
dc.date.accessioned2018-10-31T13:47:16Z
dc.date.available2018-10-31T13:47:16Z
dc.date.issued2016-06
dc.identifier.citationKang, B., Yerima, S. Y., McLaughlin, K., Sezer, S. (2016) N-opcode analysis for android malware classification and categorization. In: Proceedings of the 2016 International Conference On Cyber Security And Protection Of Digital Services (Cyber Security), London, UK. June 2016.en
dc.identifier.urihttps://pure.qub.ac.uk/portal/en/publications/nopcode-analysis-for-android-malware-classification-and-categorization(6fe45281-f302-4df8-9ccc-2acfe0059b38).html
dc.identifier.urihttp://hdl.handle.net/2086/16949
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.abstractMalware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectn-gramsen
dc.subjectmachine learningen
dc.subjectfeature selectionen
dc.subjectandroid malwareen
dc.subjectmalware detectionen
dc.subjectmalware reverse engineeringen
dc.subjectdalvik bytecodeen
dc.subjectinvasive softwareen
dc.titleN-opcode Analysis for Android Malware Classification and Categorizationen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/cybersecpods.2016.7502343
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2016en
dc.exception.reasonauthor was not DMU staff at time of publication, available on Queens Uni repositoryen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.exception.ref2021codes254aen


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