Click fraud detection rules
Łukasz Lipiński and
Michał Bernardelli ()
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Łukasz Lipiński: Cloud Technologies
Collegium of Economic Analysis Annals, 2019, issue 55, 41-54
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.
Keywords: click fraud; iForest algorithm; anomalies detection; Internet bot (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:sgh:annals:i:55:y:2019:p:41-54
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