dc.contributor.author | Kural O.E. | |
dc.contributor.author | Sahin D.O. | |
dc.contributor.author | Akleylek S. | |
dc.contributor.author | Kilic E. | |
dc.date.accessioned | 2020-06-21T09:05:24Z | |
dc.date.available | 2020-06-21T09:05:24Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 9781728139647 | |
dc.identifier.uri | https://doi.org/10.1109/UBMK.2019.8907187 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12712/2313 | |
dc.description | 4th International Conference on Computer Science and Engineering, UBMK 2019 -- 11 September 2019 through 15 September 2019 -- -- 154916 | en_US |
dc.description.abstract | With 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.iso | tur | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.isversionof | 10.1109/UBMK.2019.8907187 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Android malware | en_US |
dc.subject | feature extraction | en_US |
dc.subject | mobile malware | en_US |
dc.subject | mobile security | en_US |
dc.subject | static analysis | en_US |
dc.title | Permission Weighting Approaches in Permission Based Android Malware Detection | en_US |
dc.title.alternative | Izin Tabanli Android Kotucul Amafh Yazilim Tespitinde Izin Agirliklandirma Yaklaimlan | en_US |
dc.type | conferenceObject | en_US |
dc.contributor.department | OMÜ | en_US |
dc.identifier.startpage | 134 | en_US |
dc.identifier.endpage | 139 | en_US |
dc.relation.journal | UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |