Detecting Frauds in Online Advertising Systems

  • Sanjay Mittal
  • Rahul Gupta
  • Mukesh Mohania
  • Shyam K. Gupta
  • Mizuho Iwaihara
  • Tharam Dillon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4082)


Online advertising is aimed to promote and sell products and services of various companies in the global market through internet. In 2005, it was estimated that companies spent $10B in web advertisements, and it is expected to grow by 25-30% in the next few years. The advertisements can be displayed in the search results as sponsored links, on the web sites, etc. Further, these advertisements are personalized based on demographic targeting or on information gained directly from the user. In a standard setting, an advertiser provides the publisher with its advertisements and they agree on some commission for each customer action. This agreement is done in the presence of Internet Advertising commissioners, who represent the middle person between Internet Publishers and Internet Advertisers. The publisher, motivated by the commission paid by the advertisers, displays the advertisers’ links in its search results. Since each player in this scenario can earn huge revenue through this procedure, there is incentive to falsely manipulate the procedure by extracting forbidden information of the customer action. By passing this forbidden information to the other party, one can generate extra revenue. This paper discusses an algorithm for detecting such frauds in web advertising networks.


Search Engine Association Rule Internet Service Provider Fraud Detection Customer Action 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Reiter, M.K., Anupam, V., Mayer, A.: Detecting Hit Shaving in Click-Through Payment Schemes. In: Proceedings of the 3rd USENIX Workshop on Electronic Commerce, Boston, USA, pp. 155–166 (1998)Google Scholar
  2. 2.
    Anupam, V., Mayer, A., Nissim, K., Pinkas, B., Reiter, M.K.: On the security of pay-per-click and other Web advertising schemes. In: Proceedings of the 8th WWW International World Wide Web Conference, Toronto, Canada, pp. 1091–1100 (1999)Google Scholar
  3. 3.
    Metwally, A., Agrawal, D., El Abbadi, A.: Using Association Rules for Fraud Detection in Web Advertising Networks. In: Proceedings of the 31st International Conference on Very Large Databases (VLDB), Trondheim, Norway (2005)Google Scholar
  4. 4.
    Anupam, V., Mayer, A.: Secure Web scripting. IEEE Internet Computing 2(6), 46–55 (1998)CrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp. 487–499 (1994)Google Scholar
  6. 6.
    Anupam, V., Mayer, A.: Security of web browser scripting languages: Vulnerabilities, Attacks and Remedies. In: Proceedings of the 7th USENIX Security Symposium, San Antonio, Texas, USA (1998)Google Scholar
  7. 7.
    Gordon, M.E., De Lima-Turner, K.: Consumer attitudes towards Internet advertising - A social contract perspective. International Marketing Review 14(5), 362–375 (1997)CrossRefGoogle Scholar
  8. 8.
    Hoffman, D.L., Novak, T.P.: Advertising Pricing Models for the World Wide Web. In: Internet Publishing and Beyond: The Economics of Digital Information and Intellectual Property. MIT Press, Cambridge (2000)Google Scholar
  9. 9.
    Feng, J., Bhargava, H.K., Pennock, D.M.: Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms. INFORMS Journal on Computing (2006)Google Scholar
  10. 10.
    Seda, C.: Search Engine Advertising: Buying Your Way to the Top to Increase Sales. New Riders Press (2004)Google Scholar
  11. 11.
    Metwally, A., Agrawal, D., El Abbadi, A.: Duplicate Detection in Click Streams. In: Proceedings of the 14th WWW International World Wide Web Conference, pp. 12–21 (2005)Google Scholar
  12. 12.
    iProspect Search Engine User Attitudes Survey (2004), available at:
  13. 13.
    The Carmel Group Market Research,
  14. 14.
    Gartner, Jupiter Research,
  15. 15.
    Cybersource Reports,
  16. 16.
    ZDNet Research Study,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sanjay Mittal
    • 1
  • Rahul Gupta
    • 1
  • Mukesh Mohania
    • 2
  • Shyam K. Gupta
    • 1
  • Mizuho Iwaihara
    • 3
  • Tharam Dillon
    • 4
  1. 1.Dept of Computer ScienceI.I.T. DelhiNew DelhiIndia
  2. 2.IBM India Research LabI.I.T. DelhiNew DelhiIndia
  3. 3.Dept of Social InformaticsKyoto UniversityKyotoJapan
  4. 4.Faculty of Information TechnologyUniversity of TechnologySydneyAustralia

Personalised recommendations