Automatic privacy leakage detection for massive android apps via a novel hybrid approach

Abstract

Android apps frequently leak private data off the device with or without intentions. Researchers have proposed a large number of methods, for example, static and dynamic analysis methods, to pick out the apps which tend to leak private data. However, they are only able to identify part of private data leakage vulnerabilities, due to the dynamic features in codes or code coverage problem. This paper presents a novel hybrid approach that can find out more private data leakages than the existing static or dynamic methods. The approach, realized in a tool, called HybriDroid, which employs both static and dynamic analysis methods to extract the models of each apps, and then refines the behavior model to a more adequate one according to the dynamic analysis result. As a consequence, HybriDroid inherits the advantages of both static and dynamic analysis methods, which not only achieves a high code coverage, but also can deal with the dynamic features in codes. The evaluation results show that HybriDroid is effective in detecting privacy leakages for both inter- and intra-app communication. Comparing with the existing methods, it can achieve considerable improvements in data leakage detection performance with a 97.8% precision and 90% recall on the selected apps from DroidBench 3.0 test suite.