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The Underground Economy of Fake Antivirus Software

  • Brett Stone-GrossEmail author
  • Ryan Abman
  • Richard A. Kemmerer
  • Christopher Kruegel
  • Douglas G. Steigerwald
  • Giovanni Vigna
Conference paper

Abstract

Fake antivirus (AV) programs have been utilized to defraud millions of computer users into paying as much as one hundred dollars for a phony software license. As a result, fake AV software has evolved into one of the most lucrative criminal operations on the Internet. In this paper, we examine the operations of three large-scale fake AV businesses, lasting from three months to more than two years. More precisely, we present the results of our analysis on a trove of data obtained from several backend servers that the cybercriminals used to drive their scam operations. Our investigations reveal that these three fake AV businesses had earned a combined revenue of more than $130 million dollars. A particular focus of our analysis is on the financial and economic aspects of the scam, which involves legitimate credit card networks as well as more dubious payment processors. In particular, we present an economic model that demonstrates that fake AV companies are actively monitoring the refunds (chargebacks) that customers demand from their credit card providers. When the number of chargebacks increases in a short interval, the fake AV companies react to customer complaints by granting more refunds. This lowers the rate of chargebacks and ensures that a fake AV company can stay in business for a longer period of time.9pc]First author has been considered as the corresponding author. Please check. However, this behavior also leads to unusual patterns in chargebacks, which can potentially be leveraged by vigilant payment processors and credit card companies to identify and ban fraudulent firms.

Keywords

Credit Card Criminal Organization Underground Economy Proxy Node Credit Card Company 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported by the Office of Naval Research (ONR) under Grant N000140911042 and by the National Science Foundation (NSF) under grants CNS-0845559 and CNS-0905537. We would also like to thank the anonymous reviewers for their valuable suggestions and insights.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Brett Stone-Gross
    • 1
    Email author
  • Ryan Abman
    • 1
  • Richard A. Kemmerer
    • 2
  • Christopher Kruegel
    • 1
  • Douglas G. Steigerwald
    • 1
  • Giovanni Vigna
    • 1
  1. 1.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA
  2. 2.Department of EconomicsUniversity of CaliforniaSanta BarbaraUSA

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