检测Android应用程序中的第三方库,精度高,召回率高

Yuan Zhang, Jiarun Dai, Xiaohan Zhang, S. Huang, Zhemin Yang, Min Yang, Hao Chen
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引用次数: 49

摘要

第三方库广泛用于Android应用程序,以简化开发和增强功能。然而,合并的库也给宿主应用程序带来了新的安全和隐私问题,并且模糊了应用程序代码和库代码之间的记账。在这种情况下,需要一个精确可靠的库检测器。实际上,库代码可以由开发人员在集成期间定制,而无用的库代码可以在应用程序构建过程中被代码混淆器消除。然而,现有的图书馆检测研究并没有很好地处理这些问题,因此在实践中面临着严重的局限性。在本文中,我们提出LibPecker,一个用于Android应用程序的混淆弹性,高精度和可靠的库检测器。LibPecker采用签名匹配来给出给定库和应用程序之间的相似度评分。通过充分利用库中的内部类依赖,LibPecker为每个类生成严格的签名。为了尽可能地容忍库代码的定制和消除,LibPecker在计算库相似度时引入了自适应类相似度阈值和加权类相似度评分。为了定量地评估LibPecker的精确度和召回率,我们用大量的库和应用程序执行了第一个这样的实验(据我们所知)。结果表明,LibPecker在召回率和准确率方面都明显优于最先进的工具(分别为91%和98.1%)。
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Detecting third-party libraries in Android applications with high precision and recall
Third-party libraries are widely used in Android applications to ease development and enhance functionalities. However, the incorporated libraries also bring new security & privacy issues to the host application, and blur the accounting between application code and library code. Under this situation, a precise and reliable library detector is highly desirable. In fact, library code may be customized by developers during integration and dead library code may be eliminated by code obfuscators during application build process. However, existing research on library detection has not gracefully handled these problems, thus facing severe limitations in practice. In this paper, we propose LibPecker, an obfuscation-resilient, highly precise and reliable library detector for Android applications. LibPecker adopts signature matching to give a similarity score between a given library and an application. By fully utilizing the internal class dependencies inside a library, LibPecker generates a strict signature for each class. To tolerate library code customization and elimination as much as possible, LibPecker introduces adaptive class similarity threshold and weighted class similarity score when calculating library similarity. To quantitatively evaluate the precision and the recall of LibPecker, we perform the first such experiment (to the best of our knowledge) with a large number of libraries and applications. Results show that LibPecker significantly outperforms the state-of-the-art tools in both recall and precision (91% and 98.1% respectively).
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