Greystar: fast and accurate detection of SMS spam numbers in large cellular networks using grey phone space

  • Authors:
  • Nan Jiang;Yu Jin;Ann Skudlark;Zhi-Li Zhang

  • Affiliations:
  • University of Minnesota;AT&T Labs;AT&T Labs;University of Minnesota

  • Venue:
  • SEC'13 Proceedings of the 22nd USENIX conference on Security
  • Year:
  • 2013

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Abstract

In this paper, we present the design of Greystar, an innovative defense system for combating the growing SMS spam traffic in cellular networks. By exploiting the fact that most SMS spammers select targets randomly from the finite phone number space, Greystar monitors phone numbers from the grey phone space (which are associated with data only devices like laptop data cards and machine-to-machine communication devices like electricity meters) and employs a novel statistical model to detect spam numbers based on their footprints on the grey phone space. Evaluation using five month SMS call detail records from a large US cellular carrier shows that Greystar can detect thousands of spam numbers each month with very few false alarms and 15% of the detected spam numbers have never been reported by spam recipients. Moreover, Greystar is much faster in detecting SMS spam than existing victim spam reports, reducing spam traffic by 75% during peak hours.