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Mobile databases are the statutory backbones of many applications onsmartphones, and they store a lot of sensitive information. However,vulnerabilities in the operating system or the app logic can lead to sensitivedata leakage by giving the adversaries unauthorized access to the app'sdatabase. In this paper, we study such vulnerabilities to define a threatmodel, and we propose an OS-version independent protection mechanism that appdevelopers can utilize to detect such attacks. To do so, we model the userbehavior with the database query workload created by the original apps. Here,we model the drift in behavior by comparing probability distributions of thequery workload features over time. We then use this model to determine if theapp behavior drift is anomalous. We evaluate our framework on real-worldworkloads of three different popular Android apps, and we show that our systemwas able to detect more than 90% of such attacks.

Detecting Data Leakage from Databases on Android Apps with Concept Drift