SOUFI, ABOUBAKER SEDDIKBOUAZIZ, AZEDDINESupervisor: SAOUDI, LALIA2022-07-202022-07-202022-06-10http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/30851With the advent of communication technologies and the extended availability of smart devices (smartphones, PDAs), mobiles applications are becoming part of our everyday life, due to its portability, availability and high performance services to cover most user’s needs. Like all web applications, mobile system is exposed to malware attacks. In this context, our project is aimed at developing a mobile malware detector (MobMal detector) on Android platform using machine learning approach to detect malware by mining the patterns of Permissions and API Calls.enMobile application ‘s malware, Android Permission, API Calls, Malware Apps, Support Vector Machines.Thesis