Applying machine learning classifiers to dynamic Android malware detection at scale

Abstract

The widespread adoption and contextually sensitive nature of smartphone devices has increased concerns over smartphone malware. Machine learning classifiers are a current method for detecting malicious applications on smartphone systems. This paper presents the evaluation of a number of existing classifiers, using a dataset containing thousands of real (i.e. not synthetic) applications. We also present our STREAM framework, which was developed to enable rapid large-scale validation of mobile malware machine learning classifiers.