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dc.contributor.authorYerima, Suleimanen
dc.contributor.authorSezer, Sakiren
dc.contributor.authorAlzaylaee, Mohammed K.en
dc.date.accessioned2018-10-31T12:11:21Z
dc.date.available2018-10-31T12:11:21Z
dc.date.issued2017-03-24
dc.identifier.citationAlzaylaee, M. K., Yerima, S. Y. and Sezer, S. (2017) EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning. In: IWSPA '17: Proceedings of the 3rd ACM International Workshop on Security And Privacy Analytics, New York: ACM.en
dc.identifier.isbn9781450349093
dc.identifier.urihttp://hdl.handle.net/2086/16941
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.abstractThe Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious activities from analysis tools. These include a wide range of anti-emulator techniques, where the malware programs attempt to hide their malicious activities by detecting the emulator. For this reason, countermeasures against anti-emulation are becoming increasingly important in Android malware detection. Analysis and detection based on real devices can alleviate the problems of anti-emulation as well as improve the effectiveness of dynamic analysis. Hence, in this paper we present an investigation of machine learning based malware detection using dynamic analysis on real devices. A tool is implemented to automatically extract dynamic features from Android phones and through several experiments, a comparative analysis of emulator based vs. device based detection by means of several machine learning algorithms is undertaken. Our study shows that several features could be extracted more effectively from the on-device dynamic analysis compared to emulators. It was also found that approximately 24% more apps were successfully analysed on the phone. Furthermore, all of the studied machine learning based detection performed better when applied to features extracted from the on-device dynamic analysis.en
dc.language.isoenen
dc.publisherACMen
dc.subjectmachine learningen
dc.subjectmalware detectionen
dc.subjectandroid malwareen
dc.subjectdynamic analysisen
dc.subjectsmartphone securityen
dc.subjectapplication securityen
dc.subjectmalware mitigationen
dc.subjectanti-emulationen
dc.subjectanti-analysisen
dc.titleEMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learningen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1145/3041008.3041010
dc.researchgroupCyber Technology Institute (CTI)en
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017en
dc.exception.reasonauthor was not DMU staff at time of publicationen
dc.researchinstituteCyber Technology Institute (CTI)en


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