An Advanced Ensemble Learning Framework for Ad Click Fraud Detection using Deep Neural Networks and Gradient Boosting

Abstract

Click fraud remains one of the most pressing threats in digital advertising, inflating campaign costs and distorting marketing analytics. Conventional detection approaches are frequently unable to adapt to sophisticated and evolving fraud patterns. This study proposes an explainable hybrid framework that integrates deep learning and gradient boosting for ad click fraud detection using the Kaggle Ad Click Fraud Detection Dataset (5,000 records, 21 features). During preprocessing, multicollinearity was systematically removed through Pearson correlation analysis (threshold ρ > 0.85), reducing the feature set from 21 to 14 highly discriminative attributes. Random Under-Sampling (RUS) and SMOTE were applied to correct class imbalance. Thirteen classical machine learning and deep learning models were benchmarked, including CNN, DNN, RNN, LSTM, GRU, and hybrid LSTM–GRU networks. A Voting Classifier combining XGBoost and Bagging with Decision Tree served as the proposed architecture, achieving 100% accuracy, precision, recall, and F1-score. Explainability was incorporated through LIME (local, per-prediction waterfall explanations) and SHAP (global feature importance rankings). A Flask-based web interface enables real-time fraud prediction. The architecture demonstrates inference latency below 50 ms per request, making it suitable for large-scale, real-time advertising platforms.