An Enhanced RNN-LSTM Model for Accurate and Real-Time Click Fraud Detection in Online Advertising

Abstract - Click fraud remains a critical threat in online advertising, leading to inflated costs and undermining campaign effectiveness by diverting budgets toward illegitimate activity. Existing solutions leveraging machine learning and deep learning models have shown promise, but many still struggle with identifying subtle behavioral patterns in fraudulent clicks. In this work, we propose a robust LSTM-based Recurrent Neural Network (RNN) framework designed to enhance the detection of fraudulent click activity by modeling sequential patterns and time-dependent features in user interaction data. A comprehensive preprocessing pipeline was developed, including timestamp decomposition, feature scaling, and label encoding to ensure optimal input representation. Our model was trained and evaluated against a carefully engineered dataset enriched with behavioral and contextual click features. Among various deep learning architectures examined, including Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), the RNN-LSTM model demonstrated superior performance, achieving 99% accuracy with high precision and recall scores. The results validate the effectiveness of temporal modeling in identifying fraudulent click patterns and highlight the LSTM model’s suitability for deployment in real-time fraud detection systems. This study not only advances existing anti-fraud mechanisms but also sets a strong foundation for future work in intelligent online ad verification and fraud prevention. Key Words: Click Fraud Detection, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Deep Learning, Online Advertising, Temporal Pattern Recognition, Behavioral Analysis, Real-Time Prediction