Characterizing Promotional Attacks in Mobile App Store

  • Bo Sun
  • Xiapu Luo
  • Mitsuaki Akiyama
  • Takuya Watanabe
  • Tatsuya Mori
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 719)

Abstract

Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile apps. When users look for an app of interest, they can acquire useful data from the app store to facilitate their decision on installing the app or not. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Unfortunately, miscreants also exploit such channels to conduct promotional attacks (PAs) that lure victims to install malicious apps. In this paper, we propose and develop a new system called PADetective to detect miscreants who are likely to be conducting promotional attacks. Using a dataset with 1,723 of labeled samples, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied PADetective to a large dataset for characterizing the prevalence of PAs in the wild and find 289 K potential PA attackers who posted reviews to 21 K malicious apps.

Keywords

Mobile app store Promotional attacks Machine learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Bo Sun
    • 1
  • Xiapu Luo
    • 2
  • Mitsuaki Akiyama
    • 3
  • Takuya Watanabe
    • 3
  • Tatsuya Mori
    • 1
  1. 1.Department of Computer Science and Communications EngineeringWaseda UniversityShinjukuJapan
  2. 2.Department of ComputingThe Hong Kong Polytechnic UniversityKowloonHong Kong
  3. 3.NTT Secure Platform LaboratoriesNTT CorporationTokyoJapan

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