Corpus ID: 37611323

Random forests for the detection of click fraud in online mobile advertising

@inproceedings{Berrar2013RandomFF,
  title={Random forests for the detection of click fraud in online mobile advertising},
  author={Daniel P. Berrar},
  year={2013}
}
Click fraud is a serious threat to the pay-per-click advertising market. Here, we analyzed the click patterns associated with 3081 publishers of online mobile advertisements. The status of these publishers was known to be either fraudulent, under observation, or honest. The goal was to develop a model to predict the status of a publisher based on its individual click profile. In our study, the best model was a committee of random forests with imbalanced bootstrap sampling. The average precision… Expand

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  • Computer Science
  • Knowledge and Information Systems
  • 2015
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It is demonstrated that a particular class of learning algorithms, called click-based algorithms, are resistant to click fraud in some sense, and it is shown that other common learning algorithms are vulnerable to fraudulent attacks. Expand
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