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Securing android applications via edge assistant third-party library detection

机译:通过Edge Assistant第三方库检测保护android应用程序的安全

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摘要

Third-party library (TPL) detection in Android has been a hot topic to security researchers for a long time. A precise yet scalable detection of TPLs in applications can greatly facilitate other security activities such as TPL integrity checking, malware detection, and privacy leakage detection. Since TPLs of specific versions may exhibit their own security issues, the identification of TPL as well as its concrete version, can help assess the security of Android APPs. However in reality, existing approaches of TPL detection suffer from low efficiency for their detection algorithm to impracticable and low accuracy due to insufficient analysis data, inappropriate features, or the disturbance from code obfuscation, shrinkage, and optimization.In this paper, we present an automated approach, named PANGUARD, to detect TPLs from an enormous number of Android APPs. We propose a novel combination of features including both structural and content information for packages in APPs to characterize TPLs. In order to address the difficulties caused by code obfuscation, shrinkage, and optimization, we identify the invariants that are unchanged during mutation, separate TPLs from the primary code in APPs, and use these invariants to determine the contained TPLs as well as their versions. The extensive experiments show that PANGUARD achieves a high accuracy and scalability simultaneously in TPL detection. In order to accommodate to optimized TPL detection, which has not been mentioned by previous work, we adopt set analysis, which speed up the detection as a side effect.PANGUARD is implemented and applied on an industrial edge computing platform, and powers the identification of TPL. Beside fast detection algorithm, the edge computing deployment architecture make the detection scalable to real-time detection on a large volume of emerging APPs. Based on the detection results from millions of Android APPs, we successfully identify over 800 TPLs with 12 versions on average. By investigating the differences amongst these versions, we identify over 10 security issues in TPLs, and shed light on the significance of TPL detection with the caused harmful impacts on the Android ecosystem. (C) 2018 Elsevier Ltd. All rights reserved.
机译:长期以来,Android中的第三方库(TPL)检测一直是安全研究人员的热门话题。在应用程序中对TPL进行精确而可扩展的检测可以极大地促进其他安全活动,例如TPL完整性检查,恶意软件检测和隐私泄漏检测。由于特定版本的TPL可能会出现其自身的安全问题,因此TPL的标识及其具体版本可以帮助评估Android APP的安全性。但是现实中,由于分析数据不足,功能不当或代码混淆,缩小和优化等因素的干扰,现有的TPL检测方法由于其检测算法效率低下而无法实现,且准确性较低。一种自动方法,名为PANGUARD,可从大量的Android应用中检测TPL。我们提出了一种新颖的功能组合,包括用于APP的封装的结构和内容信息,以表征TPL。为了解决由代码混淆,缩小和优化引起的困难,我们确定了变异期间不变的不变式,将TPL与APP中的主代码分开,然后使用这些不变式来确定所包含的TPL及其版本。广泛的实验表明,PANGUARD在TPL检测中同时实现了高精度和可扩展性。为了适应先前工作中未提及的优化TPL检测,我们采用集合分析,以加快检测的副作用.PANGUARD在工业边缘计算平台上实现并应用,并为识别功能提供了支持TPL。除了快速检测算法,边缘计算部署架构还使检测可扩展到对大量新兴APP进行实时检测。基于来自数百万个Android APP的检测结果,我们成功地识别出平均12种版本的800多种TPL。通过研究这些版本之间的差异,我们确定了TPL中的10多个安全问题,并阐明了TPL检测对Android生态系统造成有害影响的重要性。 (C)2018 Elsevier Ltd.保留所有权利。

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