Hybroid: A Novel Hybrid Android Malware Detection Framework
| dc.contributor.author | Kabakuş, Abdullah Talha | |
| dc.date.accessioned | 2025-10-11T20:42:39Z | |
| dc.date.available | 2025-10-11T20:42:39Z | |
| dc.date.issued | 2021 | |
| dc.department | Düzce Üniversitesi | en_US |
| dc.description.abstract | Android, the most widely-used mobile operating system, attracts the attention of malware developers as well as benign users. Despite the serious proactive actions taken by Android, the Android malware is still widespread as a result of the increasing sophistication and the diversity of malware. Android malware detection systems are generally classified into two: (1) Static analysis, and (2) dynamic analysis. In this study, a novel Android malware detection framework, namely, Hybroid, was proposed which combines both the static and dynamic analysis techniques to benefit from the advantages of both of these techniques. An up-to-date version of Android, namely, Android Oreo, was specifically employed in order to handle the problem from an up-to-date perspective as the recent versions of Android provide new security mechanisms, which are discussed with this study. Hybroid was evaluated on a large dataset that consists of 10,658 applications, and the accuracy of Hybroid was calculated as high as 99.5% when it was utilized with the J48 classification algorithm which outperforms the state-of-the-art studies. The key findings in consequence of the experimental result are discussed in order to shed light on Android malware detection. | en_US |
| dc.identifier.doi | 10.18185/erzifbed.806683 | |
| dc.identifier.endpage | 356 | en_US |
| dc.identifier.issn | 2149-4584 | |
| dc.identifier.issue | 1 | en_US |
| dc.identifier.startpage | 331 | en_US |
| dc.identifier.uri | https://doi.org/10.18185/erzifbed.806683 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12684/21185 | |
| dc.identifier.volume | 14 | en_US |
| dc.institutionauthor | Kabakuş, Abdullah Talha | |
| dc.language.iso | en | en_US |
| dc.publisher | Erzincan Binali Yıldırım Üniversitesi | en_US |
| dc.relation.ispartof | Erzincan University Journal of Science and Technology | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.snmz | KA_DergiPark_20250911 | |
| dc.subject | Engineering | en_US |
| dc.subject | Mühendislik | en_US |
| dc.title | Hybroid: A Novel Hybrid Android Malware Detection Framework | en_US |
| dc.type | Research Article | en_US |
