A Machine Learning Approach for Real Time Android Malware Detection

Smart mobile devices are increasingly popular and common in modern life. The vast majority of these use Android - an open operating system developed by Android Inc., which Google bought in 2005. Along with its popularity, Android has become the target of rapidly developing malicious applications. Moreover, the fact that the users can install applications both from Google Play as well as through third-party application markets also facilitates the development and spread of applications as well as malwares on Android. In this paper, we propose a machine learning method to detect malicious applications on Android devices. The features used for machine learning are based on the common behavior of malicious applications, the required permissions, and other features taken from the applications files. With a data set of nearly 30000 samples including malicious and safe applications, we achieved results with 98.66% accuracy. In addition, using and changing libraries in the C++ language has helped us achieve a good processing speed and decision making. Thereby creating a framework that can run real time on normal devices such as a usual laptop.