AnDarwin: Scalable Detection of Semantically Similar Android Applications

  • Jonathan Crussell
  • Clint Gibler
  • Hao Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8134)

Abstract

The popularity and utility of smartphones rely on their vibrant application markets; however, plagiarism threatens the long-term health of these markets. We present a scalable approach to detecting similar Android apps based on their semantic information. We implement our approach in a tool called AnDarwin and evaluate it on 265,359 apps collected from 17 markets including Google Play and numerous thirdparty markets. In contrast to earlier approaches, AnDarwin has four advantages: it avoids comparing apps pairwise, thus greatly improving its scalability; it analyzes only the app code and does not rely on other information - such as the app’s market, signature, or description - thus greatly increasing its reliability; it can detect both full and partial app similarity; and it can automatically detect library code and remove it from the similarity analysis. We present two use cases for AnDarwin: finding similar apps by different developers (“clones”) and similar apps from the same developer (“rebranded”). In ten hours, AnDarwin detected at least 4,295 apps that have been the victims of cloning and 36,106 apps that are rebranded. By analyzing the clusters found by AnDarwin, we found 88 new variants of malware and identified 169 malicious apps based on differences in the requested permissions. Our evaluation demonstrates AnDarwin’s ability to accurately detect similar apps on a large scale.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jonathan Crussell
    • 1
  • Clint Gibler
    • 1
  • Hao Chen
    • 1
  1. 1.University of CaliforniaDavisUSA

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