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dc.contributor.authorKural O.E.
dc.contributor.authorSahin D.O.
dc.contributor.authorAkleylek S.
dc.contributor.authorKilic E.
dc.date.accessioned2020-06-21T09:05:24Z
dc.date.available2020-06-21T09:05:24Z
dc.date.issued2019
dc.identifier.isbn9781728139647
dc.identifier.urihttps://doi.org/10.1109/UBMK.2019.8907187
dc.identifier.urihttps://hdl.handle.net/20.500.12712/2313
dc.description4th International Conference on Computer Science and Engineering, UBMK 2019 -- 11 September 2019 through 15 September 2019 -- -- 154916en_US
dc.description.abstractWith the increasing use of mobile devices in daily life, the number of malware running on mobile devices is increasing. Increased malware may cause material and non- pecuniary damage, such as the seizure of personal information of users or the deterioration of personal data. Therefore, the need for systems that detect malware with high accuracy is increasing day by day. In this study, it is aimed to determine malware using the machine learning based static analysis technique for Android operating systems. In order to obtain high performance rates in malware detection, 14 different terms weighting techniques frequently used in text classification have been extensively adapted to this. Adapted methods were tested on 2 different datasets and compared with 3 different classification algorithms. The most successful classification result on the AMD data set was obtained from binary term weighting technique and support vector machine classification algorithm. The most successful classification result on the M0DROID data set was obtained from discriminative weighting technique and support vector machine classification algorithm. © 2019 IEEE.en_US
dc.language.isoturen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionof10.1109/UBMK.2019.8907187en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAndroid malwareen_US
dc.subjectfeature extractionen_US
dc.subjectmobile malwareen_US
dc.subjectmobile securityen_US
dc.subjectstatic analysisen_US
dc.titlePermission Weighting Approaches in Permission Based Android Malware Detectionen_US
dc.title.alternativeIzin Tabanli Android Kotucul Amafh Yazilim Tespitinde Izin Agirliklandirma Yaklaimlanen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentOMÜen_US
dc.identifier.startpage134en_US
dc.identifier.endpage139en_US
dc.relation.journalUBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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