An Ensemble Architecture Based on Deep Learning Model for Click Fraud Detection in Pay-Per-Click Advertisement Campaign

Pay-per-click (PPC) advertising has become a foundational component of digital marketing, enabling advertisers to reach targeted audiences through keyword-based ad placements. However, the rise in automated scripts, bots, and click farms has led to a surge in click fraud—invalid clicks that artificially inflate advertising costs and mislead performance metrics. Existing fraud detection techniques often rely on static rule-based systems or shallow machine learning models, which are inadequate in identifying evolving and obfuscated fraudulent behavior. This paper proposes an ensemble deep learning architecture that integrates a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network for robust click fraud detection. The CNN module captures local temporal patterns within clickstream features, while the LSTM module models long-term behavioral dependencies across user sessions. Experimental results on benchmark datasets demonstrate that the proposed hybrid model outperforms conventional models in accuracy, precision, recall, and F1-score. The ensemble approach effectively reduces false positives while adapting to evolving fraud signatures, offering a scalable and intelligent solution for securing PPC platforms.