| Popis: |
Mobile applications can access both sensitive personal data and the network, giving rise to threats of data leaks. App auditing is a fundamental program analysis task to reveal such leaks. Currently, static analysis is the de facto technique which exhaustively examines all data flows and pinpoints problematic ones. However, static analysis generates false alarms for being over-estimated and requires minutes or even hours to examine a real app. These shortcomings greatly limit the usability of automatic app auditing. To overcome these limitations, we design App Audit that relies on the synergy of static and dynamic analysis to provide effective real-time app auditing. App Audit embodies a novel dynamic analysis that can simulate the execution of part of the program and perform customized checks at each program state. App Audit utilizes this to prune false positives of an efficient but over-estimating static analysis. Overall, App Audit makes app auditing useful for app market operators, app developers and mobile end users, to reveal data leaks effectively and efficiently. We apply App Audit to more than 1,000 known malware and 400 real apps from various markets. Overall, App Audit reports comparative number of true data leaks and eliminates all false positives, while being 8.3x faster and using 90% less memory compared to existing approaches. App Audit also uncovers 30 data leaks in real apps. Our further study reveals the common patterns behind these leaks: 1) most leaks are caused by 3rd-party advertising modules, 2) most data are leaked with simple unencrypted HTTP requests. We believe App Audit serves as an effective tool to identify data-leaking apps and provides implications to design promising runtime techniques against data leaks. |
