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Towards Measuring the Role of Phone Numbers in Twitter-Advertised Spam

Published:29 May 2018Publication History

ABSTRACT

The telephony channel has become an attractive target for cyber criminals, who are using it to craft a variety of attacks. In addition to delivering voice and messaging spam, this channel is also being used to lure victims into calling phone numbers that are controlled by the attackers. One way this is done is by aggressively advertising phone numbers on social media (e.g., Twitter). This form of spam is then monetized over the telephony channel, via messages/calls made by victims. We refer to this type of attacks as outgoing phone communication (OPC) attacks.

By collecting approximately 70M tweets containing over 5,786 phone numbers over a period of 14 months, we are able to measure properties of multiple spam campaigns, including well-known tech support scams. Our contributions include a novel data collection technique that amplifies tweets containing phone numbers, clustering of tweets that are part of a given OPC attack campaign, and brief analysis of particularly interesting campaigns. We also show that some of the campaigns we analyze appear to attempt to avoid account suspension by Twitter, by including reputable URLs in their tweets. In fact, we find that Twitter suspended only about 3.5% of the accounts that participated in the top 15 spam campaigns we measured. Our results not only demonstrate a new kind of abuse exploiting the telephony channel but also show the potential benefits of using phone numbers to fight spam on Twitter.

References

  1. Faraz Ahmed and Muhammad Abulaish. 2013. A generic statistical approach for spam detection in Online Social Networks. Computer Communications Vol. 36, 10 (2013), 1120--1129.Google ScholarGoogle ScholarCross RefCross Ref
  2. Marco Balduzzi, Payas Gupta, Lion Gu, Debin Gao, and Mustaque Ahamad. 2016. MobiPot: Understanding Mobile Telephony Threats with Honeycards Proceedings of the 11th ACM SIGSAC Symposium on Information, Computer and Communications Security (ASIA CCS '16). ACM, New York, NY, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. The Journal of Machine Learning Research Vol. 3 (March. 2003), 993--1022. /dl.acm.org/citation.cfm?id=645526.657137 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Li Zhuang, John Dunagan, Daniel R. Simon, Helen J. Wang, and J. D. Tygar. 2008. Characterizing Botnets from Email Spam Records, Article 2 (2008), pages9 pages. http://dl.acm.org/citation.cfm?id=1387709.1387711Google ScholarGoogle Scholar

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  1. Towards Measuring the Role of Phone Numbers in Twitter-Advertised Spam

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      • Published in

        cover image ACM Conferences
        ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications Security
        May 2018
        866 pages
        ISBN:9781450355766
        DOI:10.1145/3196494

        Copyright © 2018 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 May 2018

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        ASIACCS '18 Paper Acceptance Rate52of310submissions,17%Overall Acceptance Rate418of2,322submissions,18%

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