Using opcode-sequences to detect malicious Android applications

Abstract

Recently, the Android platform has seen its number of malicious applications increased sharply. Motivated by the easy application submission process and the number of alternative market places for distributing Android applications, rogue authors are developing constantly new malicious programs. While current anti-virus software mainly relies on signature detection, the issue of alternative malware detection has to be addressed. In this paper, we present a feature based detection mechanism relying on opcode-sequences combined with machine learning techniques. We assess our tool on both a reference dataset known as Genome Project as well as on a wider sample of 40,000 applications retrieved from the Google Play Store.