Abstract
Due to the involvement of a huge amount of expenditure, the Pay-per-Click digital advertising model is affected by click fraud. The rare presence of illegitimate publishers than the legitimates makes the class distribution skewed which in turn biases the learning model towards the majority class. Moreover, the dynamic disguise activities of publishers make the task harder to investigate the actual status labels of the publishers, resulting in less accurate predictions. However, features reported in the literature detect click fraud based on analyzing the publishers’ conduct but are unable to cope with the changing behavior of publishers. Therefore, we proposed a framework addressing two challenging issues: a) eight new conditional features are proposed to better capture the dynamics of click fraud behavior by merging two or more attributes over publishers on finer-grained time intervals. b) Examined the impact of data sampling methods on learners’ performance for the effective identification of publisher’s activity. The performance of the classification algorithms is assessed using average precision, recall, and f-measure and validated using 10-fold cross-validation. Experimental results illustrate the potential of the proposed features over a balanced dataset in improving the average precision of the learners in discriminating the fraudsters among genuine publishers.
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Sisodia, D., Sisodia, D.S. (2023). Data Sampling Methods for Analyzing Publishers Conduct from Highly Imbalanced Dataset in Web Advertising. In: Garg, L., et al. Information Systems and Management Science. ISMS 2021. Lecture Notes in Networks and Systems, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-031-13150-9_34
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