PIndroid: A novel Android malware detection system using ensemble learning methods

Abstract

The extensive use of smartphones has been a major driving force behind a drastic increase of malware attacks. Covert techniques used by the malware make them hard to detect with signature based methods. In this paper, we present PIndroid – a novel Permissions and Intents based framework for identifying Android malware apps. To the best of our knowledge, PIndroid is the first solution that uses a combination of permissions and intents supplemented with Ensemble methods for accurate malware detection. The proposed approach, when applied to 1,745 real world applications, provides 99.8% accuracy (which is best reported to date). Empirical results suggest that the proposed framework is effective in detection of malware apps.