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Click fraud detection rules

Author

Listed:
  • Łukasz Lipiński

    (Cloud Technologies)

  • Michał Bernardelli

    (Szkoła Główna Handlowa w Warszawie, Kolegium Analiz Ekonomicznych)

Abstract

Effective detection of clicks on websites done by automatic computer programs is a valuable tool in the fight against this type of fraud and gives immediate measurable benefit in the form of savings for poorly targeted advertising. The purpose of the study described in the article was to present the unsupervised learning method, which results in a set of rules that allow for effective detection of bots that are responsible for the click frauds, assuming that their behavior differs in some aspects from human behavior. This analysis proves that involving device type as an extra variable improves the effectiveness of rules used for fraud detection and that the proposed algorithm provides a flexible and efficient solution for the given problem.

Suggested Citation

  • Łukasz Lipiński & Michał Bernardelli, 2019. "Click fraud detection rules," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 55, pages 41-54.
    • Handle: RePEc:sgh:annals:i:55:y:2019:p:41-54
    as

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    File URL: http://rocznikikae.sgh.waw.pl/p/roczniki_kae_z55_03.pdf
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    References listed on IDEAS

    as
    1. Michał Bernardelli, 2015. "Cheater detection in Real Time Bidding system – panel approach," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 39, pages 11-24.
    Full references (including those not matched with items on IDEAS)

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