Elsersy, Wael F. and Anuar, Nor Badrul and Ab Razak, Mohd Faizal (2023) Rootector: Robust android rooting detection framework using machine learning algorithms. Arabian Journal for Science and Engineering, 48 (2). pp. 1771-1791. ISSN 1319-8025, DOI https://doi.org/10.1007/s13369-022-06949-5.
Full text not available from this repository.Abstract
Recently, the newly launched Google protect service alerts Android users from installing rooting tools. However, Android users lean toward rooting their Android devices to gain unlimited privileges, which allows them to customize their devices and allows Android Apps to bypass all Android security logging and security system. Rooting is one of the most malicious tactics that is used by Android malware that offers malware with the ability to open backdoor, server ports, access the Android kernel commands, and silently install malicious App and make them irremovable and undetectable. The existing Android malware detection frameworks propose embedded root-exploit code detection within the Android App. However, most frameworks overlook the rooted device detection part. In addition, many evasion techniques are developed to cloak the rooted devices. The above facts pose the challenging tasks of rooting detection and the current studies highlighted a deficiency in root detection research. Hence, this study proposes
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Android root exploits; Rooting detection; Android Malware; Machine learning; Deep learning; Hyper-parameter optimizations |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 01 Nov 2024 01:09 |
| Last Modified: | 01 Nov 2024 01:09 |
| URI: | http://eprints.um.edu.my/id/eprint/39521 |
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