A hybrid approach for identifying non-human traffic in online digital advertising

Click fraud is a serious problem facing online advertising business. The malicious intent of clicking online ads either committed by humans or by non-humans, forced financial losses on advertisers utilizing pay-per-click advertising. Non-human traffic is usually designed to inflate web traffic for fraudulent purposes. In this paper, we demonstrate a hybrid approach consisting of two-level fingerprint applied in two phases to detect illegitimate non-human traffic. The first-level fingerprint is a pattern generated using immutable information about a user navigating a website’s pages. It will be used in the first traffic illegitimacy detection phase to infer rules about illegitimate non-human traffic from a developed ontology about web traffic legitimacy. The second-level fingerprint is generated using behavioral ad click patterns, which will be used in the second detection phase by applying a Machine-Learning (ML) algorithm. To test the proposed approach, a real commercial website for ads, called Waseet.com, was used. The access logs of the website server were utilized for the purpose of this research. The experiments show that our proposed hybrid approach using the ontology of web traffic illegitimacy and the ML k-NN classifier detects around (98.6%) of fake clicks.