LibD: scalable and precise third-party library detection in android markets

摘要: With the thriving of mobile app markets, third-party libraries are pervasively integrated in Android applications. Third-party provide functionality such as advertisements, location services, and social networking making multi-functional development much more productive. However, spread vulnerable or harmful may also hurt entire ecosystem, leading to various security problems. The platform suffers severely from problems due way its ecosystem is constructed maintained. Therefore, library identification has emerged an important problem which basis many applications repackaging detection malware analysis. According our investigation, existing work on still requires improvement aspects, including accuracy obfuscation resilience. In response these limitations, we propose a novel approach identifying libraries. Our method utilizes internal code dependencies detect classify candidates. Different most previous methods detected candidates based similarity comparison, feature hashing can better handle whose package names obfuscated. Based this approach, have developed prototypical tool called LibD evaluated it with update-to-date large-scale dataset. experimental results 1,427,395 apps show that compared tools, multi-package presence name-based obfuscation, significantly improved precision without loss scalability.