Securing android applications via edge assistant third-party library detection

Zhushou Tang, Minhui Xue, Guozhu Meng, Chengguo Ying, Yugeng Liu, Jianan He, Haojin Zhu*, Yang Liu

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)257-272
Number of pages16
JournalComputers and Security
Volume80
DOIs
Publication statusPublished - Jan 2019

Keywords

  • Third-party library detection
  • Edge computing
  • Mobile security
  • Malware detection
  • Vulnerability detection

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