Prioritizing data flows and sinks for app security transformation

摘要

There have been extensive investigations on identifying sensitive data flows in Android apps for detecting malicious behaviors. Typical real world apps have a large number of sensitive flows and sinks. Thus, security analysts need to prioritize these flows and data sinks according to their risks, i.e., flow ranking and sink ranking. In this paper, we present an efficient graph-algorithm based risk metric for prioritizing risky flows and sinks in Android grayware apps. The new risk metric is quantitative and can differentiate the sensitivities of flows and sinks in an app. In the experiments, our risk prioritization produces order-ings that are highly consistent with manual inspection. To enable post-detection security enforcement of sensitive sinks, we also present an automatic rewriting framework that utilizes the above prioritization technique. Our rewriting strategies are more feasible than the state-of-art solutions by supporting flow-and sink-based rewriting. We implement our prototype as ReDroid. ReDroid is designed for security analysts who manage organizational app repositories and customize third-party apps to satisfy organization imposed security requirements. We use ReDroid to rewrite both benchmark apps and real world gray-ware.