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dc.contributor.authorYerima, Suleimanen
dc.contributor.authorSezer, Sakiren
dc.contributor.authorKang, B.en
dc.contributor.authorMcLaughlin, K.en
dc.date.accessioned2018-10-31T12:48:42Z
dc.date.available2018-10-31T12:48:42Z
dc.date.issued2016-11
dc.identifier.citationKang, B., Yerima, S. Y., Sezer, S., McLaughlin, K. (2016) N-gram opcode analysis for Android malware detection. International Journal on Cyber Situational Awareness, 1(1), pp. 231-255.en
dc.identifier.issn2057-2182
dc.identifier.urihttps://www.c-mric.com/1001011
dc.identifier.urihttp://hdl.handle.net/2086/16945
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 URI link.en
dc.description.abstractAndroid malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed achieved an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends.en
dc.language.isoenen
dc.subjectandroid malwareen
dc.subjectmalware detectionen
dc.subjectn-gramen
dc.subjectmachine learningen
dc.subjectfeature selectionen
dc.subjectopcodeen
dc.subjectdalvik bytecodeen
dc.titleN-gram Opcode Analysis for Android Malware Detectionen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.22619/ijcsa.2016.1001011
dc.researchgroupCyber Technology Institute (CTI)en
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2016en
dc.exception.reasonauthor was not DMU staff at time of publication, Open access journalen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.exception.ref2021codes254aen


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