AppAngio: Revealing Contextual Information of Android App Behaviors by API-Level Audit Logs
This work addresses security threats for Android users and device manufacturers by providing a tool to analyze app behaviors, though it appears incremental as it builds on existing log analysis methods.
The authors tackled the problem of analyzing Android app behaviors to identify malicious activities by proposing AppAngio, a system that reveals contextual information using API-level audit logs, with experiments showing it assists analysts in identifying malice and incurs negligible performance overhead.
Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps' audit logs is one of the essential methods for some device manufacturers to unveil the underlying malice within apps. We propose and implement AppAngio, a novel system that reveals contextual information in Android app behaviors by API-level audit logs. Our goal is to help analysts of device manufactures understand what has happened on users' devices and facilitate the identification of the malice within apps. The key module of AppAngio is identifying the path matched with the logs on the app's control-flow graph (CFG). The challenge, however, is that the limited-quantity logs may incur high computational complexity in the log matching, where there are a large number of candidates caused by the coupling relation of successive logs. To address the challenge, we propose a divide and conquer strategy that precisely positions the nodes matched with log records on the corresponding CFGs and connects the nodes with as few backtracks as possible. Our experiments show that AppAngio reveals the contextual information of behaviors in real-world apps. Moreover, the revealed results assist the analysts in identifying malice of app behaviors and complement existing analysis schemes. Meanwhile, AppAngio incurs negligible performance overhead on the Android device.