Bayazit, Esra ÇalıkŞAHİNGÖZ, ÖZGÜR KORAYDoğan, Buket2022-11-292022-11-292020E. C. Bayazit, O. Koray Sahingoz and B. Dogan, "Malware Detection in Android Systems with Traditional Machine Learning Models: A Survey," 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2020.978-1-7281-9352-6https://doi.org/10.1109/HORA49412.2020.9152840https://hdl.handle.net/11413/7985Due to the increased number of mobile devices, they are integrated in every dimension of our daily life. To execute some sophisticated programs, a capable operating must be set up on them. Undoubtedly, Android is the most popular mobile operating system in the world. IT is extensively used both in smartphones and tablets with an open source manner which is distributed with Apache License. Therefore, many mobile application developers focused on these devices and implement their products. In recent years, the popularity of Android devices makes it a desirable target for malicious attackers. Especially sophisticated attackers focused on the implementation of Android malware which can acquire and/or utilize some personal and sensitive data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. In this work, we aimed to analyze the algorithms which are used in malware detection and making a comparative analysis of the literature. With this study, it is intended to produce a comprehensive survey resource for the researchers, which aim to work on malware detection.eninfo:eu-repo/semantics/restrictedAccessMachine LearningAndroid SystemMalware DetectionSurveyconferenceObject0006444043000652-s2.0-85089675361