Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3390
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dc.contributor.authorTirkey, Anand-
dc.contributor.authorMohapatra, Ramesh Kumar-
dc.contributor.authorKumar, Lov-
dc.date.accessioned2019-12-18T05:39:25Z-
dc.date.available2019-12-18T05:39:25Z-
dc.date.issued2019-12-
dc.identifier.citationThe 26th Asia-Pacific Software Engineering Conference (APSEC 2019) Putrajaya, Malaysia, 2-5 December 2019en_US
dc.identifier.urihttp://hdl.handle.net/2080/3390-
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractAndroid OS being the popular choice of majority users also faces the constant risk of breach of confidentiality, integrity and availability (CIA). Effective mitigation efforts needs to identified in order to protect and uphold the CIA triad model, within the android ecosystem. In this paper, we propose a novel method of android malware classification using Object-Oriented Software Metrics and machine learning algorithms. First, android apps are decompiled and Object-Oriented Metrics are obtained. VirusShare service is used to tag an app either as malware or benign. ObjectOriented Metrics and malware tag are clubbed together into a dataset. Eighty different machine-learned models are trained over five thousand seven hundred and seventy four android apps. We evaluate the performance and stability of these models using it’s malware classification accuracy and AUC (area under ROC curve) values. Our method yields an accuracy and AUC of 99.83% and 1.0 respectively.en_US
dc.subjectAndroiden_US
dc.subjectMalware detectionen_US
dc.subjectMachine learningen_US
dc.subjectObject-oriented metricsen_US
dc.titleAnatomizing Android Malwaresen_US
dc.typeArticleen_US
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