EnsembleDroid: A Malware Detection Approach for Android System based on Ensemble Learning

In recent years, mobile devices such as smartphones and tablets have become one of the most popular digital devices of choice in our daily lives when it comes to functionality and convenience. With only ever increasing popularity, mobile devices with Android operating systems have become a common target for malware especially through third-party markets. To make things worse, the emergence of obfuscation and adversarial example attacks enables malware to evade traditional security methods and steal a user’s private information. In this paper, we propose an ensemble learning-based framework for detecting malware by using risky permissions as features to train a classifier and determine whether a mobile application (a.k.a. app) is malware or not. To evaluate the performance of the proposed malware detection approach, we have conducted a series of experiments using real world Android app datasets that are composed of both malicious and benign apps. Experimental results clearly show that the proposed malware detection approach can effectively detect malware with a high accuracy.