Kabakuş, Abdullah Talha2020-04-302020-04-3020191392-124Xhttps://doi.org/10.5755/j01.itc.48.2.21457https://hdl.handle.net/20.500.12684/5394WOS: 000510402300005Malicious applications are widespread for Android despite the taken serious actions by the operating system. Static and dynamic analysis techniques are utilized to detect malware by identifying the signatures of malicious applications by inspecting both the resources and behaviors of malware, respectively. In this study, what static analysis can utmost offer to detect malware in Android ecosystem is discussed and experimented on commonly used datasets in the literature by proposing a novel Android malware detection approach based on static analysis techniques. With the proposed study, the effectiveness of novel static analysis features' in terms of detecting malware in Android ecosystem are proved. These features were underestimated by the related work in the literature. The experimental result shows that the proposed Android malware detection approach is very effective in terms of detecting Android malware. Each feature used by the proposed approach is evaluated by using different types of machine learning techniques in order to highlight its impact on detecting malware and inform the digital investigators. The accuracy of the proposed static analysis approach is calculated as high as 0.987 for 10,865 applications.en10.5755/j01.itc.48.2.21457info:eu-repo/semantics/openAccessAndroid malwareAndroid malware detectionstatic analysismachine learningAndroidArticle482235249WOS:000510402300005Q3Q4