Show me the money: Characterizing spam-advertised revenue

Chris Kanich, Nicholas Weaver, Damon McCoy, Tristan Halvorson, Christian Kreibich, Kirill Levchenko, Vern Paxson, Geoffrey M. Voelker, Stefan Savage

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

    Abstract

    Modern spam is ultimately driven by product sales: Goods purchased by customers online. However, while this model is easy to state in the abstract, our understanding of the concrete business environment-how many orders, of what kind, from which customers, for how much-is poor at best. This situation is unsurprising since such sellers typically operate under questionable legal footing, with "ground truth" data rarely available to the public. However, absent quantifiable empirical data, "guesstimates" operate unchecked and can distort both policy making and our choice of appropriate interventions. In this paper, we describe two inference techniques for peering inside the business operations of spam-advertised enterprises: Purchase pair and basket inference. Using these, we provide informed estimates on order volumes, product sales distribution, customer makeup and total revenues for a range of spamadvertised programs.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 20th USENIX Security Symposium
    PublisherUSENIX Association
    Pages219-233
    Number of pages15
    ISBN (Electronic)9781931971874
    StatePublished - 2011
    Event20th USENIX Security Symposium - San Francisco, United States
    Duration: Aug 8 2011Aug 12 2011

    Publication series

    NameProceedings of the 20th USENIX Security Symposium

    Conference

    Conference20th USENIX Security Symposium
    Country/TerritoryUnited States
    CitySan Francisco
    Period8/8/118/12/11

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Safety, Risk, Reliability and Quality

    Fingerprint

    Dive into the research topics of 'Show me the money: Characterizing spam-advertised revenue'. Together they form a unique fingerprint.

    Cite this