Android Malware Detection and Categorization using Machine Learning

Abstract

Android became one of the most widely used mobile operating system, and the amount of malware targeting it is increasing at an alarming rate. Despite the fact that notable studies on malware detection and classification have been conducted in academia and industry, but a robust and efficient solution for detection of all types of Android malwares is still a challenge. Existing solutions do not adequately consider factors like concept drift and are often not based on a hybrid approach. Also they have been designed using infor mation collected by running malware samples on virtual environment (and not on a real device). Thus, they are not able to detect sophisticated or new malwares. In this research work we have studied existing solutions and after finding their limitations we have proposed an effective and efficient hybrid Android malware detection solution based on machine learning to detect and categorize existing, emerging and behaviour evolving Android malwares.