International Conference on Neural Information Processing

Neural Information Processing pp 663-673 | Cite as

Fine-Grained Risk Level Quantication Schemes Based on APK Metadata

  • Takeshi Takahashi
  • Tao Ban
  • Takao Mimura
  • Koji Nakao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9491)

Abstract

The number of security incidents faced by Android users is growing, along with a surge in malware targeting Android terminals. Such malware arrives at the Android terminals in the form of Android Packages (APKs). Various techniques for protecting Android users from such malware have been reported, but most of them have focused on the APK files themselves. Unlike these approaches, we use Web information obtained from online APK markets to improve the accuracy of malware detection. In this paper, we propose category/cluster-based APK analysis schemes that quantify the risk of an APK. The category-based scheme uses category information available on the Web, whereas the cluster-based method uses APK descriptions to generate clusters of APK files. In this paper, the performance of the proposed schemes is verified by comparing their area under the curve values with that of a conventional scheme; moreover, the usability of Web information for the purpose of better quantifying the risks of APK files is confirmed.

Keywords

Android Package APK Malware Static analysis Security 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Takeshi Takahashi
    • 1
  • Tao Ban
    • 1
  • Takao Mimura
    • 2
  • Koji Nakao
    • 1
  1. 1.National Institute of Information and Communications TechnologyTokyoJapan
  2. 2.Secure Brain CorporationTokyoJapan

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