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dc.contributor.authorOgwara, NOen_NZ
dc.contributor.authorPetrova, Ken_NZ
dc.contributor.authorYang, MLBen_NZ
dc.date.accessioned2021-05-23T23:01:23Z
dc.date.available2021-05-23T23:01:23Z
dc.date.copyright2020-11-30en_NZ
dc.identifier.citation2020 30th International Telecommunication Networks and Applications Conference (ITNAC), 2020, pp. 1-6, doi: 10.1109/ITNAC50341.2020.9315052.
dc.identifier.urihttp://hdl.handle.net/10292/14205
dc.description.abstractWe propose an intelligent malware detection system (MOBDroid) that aims to protect the end-user's mobile device (MD) in mobile cloud computing (MCC) environment. MOBDroid utilizes the Android Operating System (OS) permission-based security system. The APK files of 28,306 benign and malicious applications (apps) collected from the AndroZoo and RmvDroid malware repositories were used in the system development process. The apps were decompiled in order to extract their manifest files and construct a dataset comprising the permissions requested by each of the apps. We identified some unique permissions that could be used to distinguish between malicious and benign apps and performed a series of experiments using a machine learning (ML) model; the model drew on the ML.net library and was implemented in C#.net. In the experiments conducted, we obtained classification accuracy of 96.89%, a detection rate of 98.65%, and false negative rate of 1.35%. The results indicate that our model compares very favorably to other models reported in the extant literature.
dc.publisherIEEE
dc.relation.urihttps://ieeexplore.ieee.org/document/9315052
dc.rightsCopyright © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectMOBDroid; Mobile cloud computing; Malware detection; Data security; Mobile devices; Machine learning
dc.titleMOBDroid: An Intelligent Malware Detection System for Improved Data Security in Mobile Cloud Computing Environmentsen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1109/ITNAC50341.2020.9315052en_NZ
aut.relation.pages6
pubs.elements-id395184
aut.filerelease.date2022-11-30
aut.relation.conference2020 30 th International Telecommunication Networks and Applications Conference) (ITNAC)en_NZ


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