Divide and Conquer: Recovering Contextual Information of Behaviors in Android Apps Around Limited-Quantity Audit Logs
Zhaoyi Meng, Yan Xiong, Wenchao Huang, Fuyou Miao, Taeho Jung, Jianmeng Huang · 2019
We propose and implement DroidHolmes, a novel system that recovers contextual information of app behaviors around limited-quantity audit logs. The key module of DroidHolmes is identifying the path matched with 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 in matching successive logs. To address the challenge, we propose a divide and conquer algorithm to individually position each node on the CFG matched with logs. In our experiments, DroidHolmes recovers contextual information in the behaviors of real-world apps. Meanwhile, DroidHolmes incurs negligible performance overhead on smartphones.