- Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi
- Vol: 14 Issue: 1
- Hybroid: A Novel Hybrid Android Malware Detection Framework
Hybroid: A Novel Hybrid Android Malware Detection Framework
Authors : Abdullah Talha Kabakuş
Pages : 331-356
Doi:10.18185/erzifbed.806683
View : 8 | Download : 4
Publication Date : 2021-03-31
Article Type : Research
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.Keywords : Android malware detection, mobile malware, mobile security, static analysis, dynamic analysis, Android