LibD: Android市场中可扩展和精确的第三方库检测

Menghao Li, Wei Wang, Pei Wang, Shuai Wang, Dinghao Wu, Jian Liu, Rui Xue, Wei Huo
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引用次数: 130

摘要

随着移动应用市场的蓬勃发展,第三方库被广泛地集成到Android应用程序中。第三方库提供广告、位置服务和社交网络服务等功能,使多功能应用程序开发更加高效。然而,脆弱或有害的第三方库的传播也可能伤害整个移动生态系统,导致各种安全问题。Android平台由于其生态系统的构建和维护方式严重存在这些问题。因此,第三方Android库识别已经成为一个重要的问题,它是许多安全应用的基础,如重新包装检测和恶意软件分析。根据我们的调查,现有的Android库检测工作在很多方面还需要改进,包括准确性和抗混淆能力。针对这些限制,我们提出了一种识别第三方Android库的新方法。我们的方法利用Android应用程序的内部代码依赖来检测和分类候选库。与以往大多数基于相似性比较对检测到的候选库进行分类的方法不同,我们的方法基于特征哈希,可以更好地处理包名和方法名混淆的代码。基于这种方法,我们开发了一个名为LibD的原型工具,并使用最新的大规模数据集对其进行了评估。我们在1,427,395个应用程序上的实验结果表明,与现有工具相比,LibD可以更好地处理存在基于名称混淆的多包第三方库,从而在不损失可扩展性的情况下显著提高精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LibD: Scalable and Precise Third-Party Library Detection in Android Markets
With the thriving of the mobile app markets, third-party libraries are pervasively integrated in the Android applications. Third-party libraries provide functionality such as advertisements, location services, and social networking services, making multi-functional app development much more productive. However, the spread of vulnerable or harmful third-party libraries may also hurt the entire mobile ecosystem, leading to various security problems. The Android platform suffers severely from such problems due to the way its ecosystem is constructed and maintained. Therefore, third-party Android library identification has emerged as an important problem which is the basis of many security applications such as repackaging detection and malware analysis. According to our investigation, existing work on Android library detection still requires improvement in many aspects, including accuracy and obfuscation resilience. In response to these limitations, we propose a novel approach to identifying third-party Android libraries. Our method utilizes the internal code dependencies of an Android app to detect and classify library candidates. Different from most previous methods which classify detected library candidates based on similarity comparison, our method is based on feature hashing and can better handle code whose package and method names are obfuscated. Based on this approach, we have developed a prototypical tool called LibD and evaluated it with an update-to-date and large-scale dataset. Our experimental results on 1,427,395 apps show that compared to existing tools, LibD can better handle multi-package third-party libraries in the presence of name-based obfuscation, leading to significantly improved precision without the loss of scalability.
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