Smart Detection of Ad Click Fraud via Machine Learning and Deep Learning Algorithms

Online advertising is now a critical aspect of digital marketing, as it helps organizations to target the audience effectively. Nonetheless, an expanding ecosystem has also contributed to the increase in ad click fraud, as automatic bots or ill-intentioned individuals produce artificial clicks to inflate engagement data and generate at least thousands of dollars in losses. This paper introduces a smart and scalable model of Ad Click Fraud Detection based on both Machine Learning (ML) and Deep Learning (DL) methods. The given model utilizes the Online Advertising Digital Marketing Dataset of the Kaggle that has been preprocessed (data cleaning, encoding, normalization, and Synthetic Minority Oversampling Technique (SMOTE)) in this case to deal with the issue of class imbalance. Random Forest, Gradient Boosting, LightGBM, and Artificial Neural Network (ANN) models were compared and analyzed, and Random Forest had the best classification accuracy and strength. Moreover, a Flask-based web interface can be used to detect fraud in real-time and can be used to verify the authenticity of the selected clicks instantly. The suggested solution is very precise, scalable, and deployable and offers a viable way to overcome the problem of fraudulent ad engagements and protect advertising investment.