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The growing threat to user privacy by Android applications (app) has tremendously increased the need for more reliable and accessible analysis techniques. This paper presents AspectDroid¹—an offline app-level hybrid analysis system designed to investigate Android applications for possible unwanted activities. It leverages static bytecode instrumentation to weave in analysis routines into an existing application to provide efficient dataflow analysis, detection of resource abuse, and analytics of suspicious behaviors, which are then monitored dynamically at runtime. Unlike operating system or framework dependent approaches, AspectDroid does not require porting from one version of Android to another, nor does it depend on a particular Android runtime, making it a more adaptable and easier to use technique. We evaluate the strength of our dataflow algorithm on 105 apps from the DroidBench corpus, with …