Kabakuş, Abdullah Talha2023-07-262023-07-2620212149-4584http://doi.org/10.18185/erzifbed.806683https://search.trdizin.gov.tr/yayin/detay/479182https://hdl.handle.net/20.500.12684/12229Android, 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-todate 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 applications, and the accuracy of Hybroid was calculated as high as 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.en10.18185/erzifbed.806683info:eu-repo/semantics/openAccessArticle141331356479182