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Hey, You, Get Off of My Image: Detecting Data Residue in Android Images

  • Xiao ZhangEmail author
  • Yousra Aafer
  • Kailiang Ying
  • Wenliang Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9878)

Abstract

Android’s data cleanup mechanism has been called into question with the recently discovered data residue vulnerability. However, the existing study only focuses on one particular Android version and demands heavy human involvement. In this project, we aim to fill the gap by providing a comprehensive understanding of the data residue situation across the entire Android ecosystem. To this end, we propose ANRED(ANRED is a former French public institution for the recovery and disposal of waste.), an ANdroid REsidue Detector that performs static analysis on Android framework bytecode and automatically quantifies the risk for each identified data residue instance within collected system services. The design of ANRED has overcome several challenges imposed by the special characteristic of Android framework and data residue vulnerability. We have implemented ANRED in WALA and further evaluated it against 606 Android images. The analysis results have demonstrated the effectiveness, efficiency and reliability of ANRED. In particular, we have confirmed the effect of vendor customization and version upgrade on data residue vulnerability. We have also identified five new data residue instances that have been overlooked in the previous study, leading to data leakage and privilege escalation attacks.

Keywords

Entry Point System Service Call Graph Code Coverage Break Link 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

We greatly appreciate the insightful comments and constructive feedback from the anonymous reviewers. This project was supported in part by the NSF grant 1318814. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiao Zhang
    • 1
    Email author
  • Yousra Aafer
    • 1
  • Kailiang Ying
    • 1
  • Wenliang Du
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA

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