A Study of Grayware on Google Play

Benjamin Andow, Adwait Nadkarni, Blake Bassett, William Enck, Tao Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While there have been various studies identifying and classifying Android malware, there is limited discussion of the broader class of apps that fall in a gray area. Mobile grayware is distinct from PC grayware due to differences in operating system properties. Due to mobile grayware's subjective nature, it is difficult to identify mobile grayware via program analysis alone. Instead, we hypothesize enhancing analysis with text analytics can effectively reduce human effort when triaging grayware. In this paper, we design and implement heuristics for seven main categories of grayware. We then use these heuristics to simulate grayware triage on a large set of apps from Google Play. We then present the results of our empirical study, demonstrating a clear problem of grayware. In doing so, we show how even relatively simple heuristics can quickly triage apps that take advantage of users in an undesirable way.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE Symposium on Security and Privacy Workshops, SPW 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages224-233
Number of pages10
ISBN (Electronic)9781509008247
DOIs
StatePublished - Aug 1 2016
Event2016 IEEE Symposium on Security and Privacy Workshops, SPW 2016 - San Jose, United States
Duration: May 23 2016May 25 2016

Publication series

NameProceedings - 2016 IEEE Symposium on Security and Privacy Workshops, SPW 2016

Other

Other2016 IEEE Symposium on Security and Privacy Workshops, SPW 2016
CountryUnited States
CitySan Jose
Period5/23/165/25/16

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Artificial Intelligence

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