Android malware detection based on network traffic using decision tree algorithm

Abstract

Android mobile operating system has well developed and gained absolute popularity among user. Although android is an open source operating system, it fits user daily life requirement nowadays. However, this is the reason why android malware keep on increasing every year. There are various method used to detect the occurrence of android malware such as based on static or dynamic analysis. Static analysis is favourable approach because it is quick and inexpensive. However, the static analysis unable to monitor the malicious application behavior during runtime. Therefore, we proposed a dynamic detection technique based on network traffic which records the application behavior during runtime. We consider seven network traffic features extracted from Drebin and Contagiodumpset dataset. The Drebin dataset achieved higher accuracy value with 98.4% as compared to Contagiodumpset dataset when tested using J48 decision tree algorithm.