Chanin TansettanakornSupachai ThongprasitSiritam ThamkongkaVasaka VisoottivisethMahidol University2018-12-112019-03-142018-12-112019-03-142016-07-22Proceedings of the 2016 5th ICT International Student Project Conference, ICT-ISPC 2016. (2016), 1-52-s2.0-84991808744https://repository.li.mahidol.ac.th/handle/20.500.14594/43460© 2016 IEEE. According to the advanced wireless technology in nowadays, most people mainly use their mobile phones as an essential tool. At the same time, threats of the mobile phones such as virus, botnets, and other malware are also increasing as well. However, most users have the limited of knowledge about mobile threats. Therefore, we would like to reduce the number of botnet-infected mobile phones before the users install an application on their phones. We develop a system called ABIS (Android Botnet Identification System) to check Android applications whether they are possibly be malware or not. In order to identify the Android botnets, our system learns the characteristics of each Android botnet family from the dataset provided by the University of New Brunswick [1]. We analyze the Android APK files, extract important features, and find an appropriate machine learning technique. As the results, we found that our system can classify the Android botnets with about 96.9% of recall.Mahidol UniversityComputer ScienceConference PaperSCOPUS10.1109/ICT-ISPC.2016.7519221