Abstract
Click fraud, the manipulation of online advertisement traffic figures, is becoming a major concern for businesses that advertise online. This can lead to financial losses and inaccurate click statistics. To address this problem, it is essential to have a reliable method for identifying click fraud. This includes distinguishing between legitimate clicks made by users and fraudulent clicks generated by bots or other software, which enables companies to advertise their products safely. The XGBoost model was trained on the TalkingData AdTracking Fraud Detection dataset from Kaggle, using binary classification to predict the likelihood of a click being fraudulent. The importance of data treatment was taken into consideration in the model training process, by carefully preprocessing and cleaning the data before feeding it into the model. This helped to improve the accuracy and performance of the model by reaching an AUC of 0.96 and LogLoss of 0.15.
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Sahllal, N., Souidi, E.M. (2023). Forecasting Click Fraud via Machine Learning Algorithms. In: El Hajji, S., Mesnager, S., Souidi, E.M. (eds) Codes, Cryptology and Information Security. C2SI 2023. Lecture Notes in Computer Science, vol 13874. Springer, Cham. https://doi.org/10.1007/978-3-031-33017-9_18
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