AD CLICK FRAUD DETECTION WITH ML AND DL

Ad click fraud has become a major challenge in the digital advertising ecosystem, leading to significant financial losses for advertisers and reducing the reliability of online advertising metrics. Fraudulent clicks are often generated using automated bots, click farms, or malicious scripts designed to inflate advertising revenue or exhaust competitors’ advertising budgets. With the rapid growth of payper-click advertising models, detecting and preventing such fraudulent activities has become critical. Traditional rule-based detection methods often fail to identify sophisticated fraud patterns due to their inability to adapt to evolving attack strategies. Machine Learning (ML) and Deep Learning (DL) techniques provide more effective solutions by analyzing large volumes of user interaction data and identifying abnormal behavioral patterns. This study proposes a fraud detection framework that utilizes supervised machine learning algorithms and deep neural networks to detect suspicious click patterns in real time. The system processes historical clickstream data, user interaction logs, and network attributes to train predictive models capable of distinguishing legitimate clicks from fraudulent ones. Feature engineering techniques are applied to capture temporal patterns, device information, IP behavior, and session characteristics. The proposed framework combines classification algorithms such as Random Forest, Support Vector Machine, and Gradient Boosting with deep learning architectures like Artificial Neural Networks to improve detection accuracy. Experimental evaluation demonstrates that the hybrid approach significantly enhances fraud detection performance compared with traditional approaches. The system can assist digital advertising platforms in minimizing revenue loss, improving transparency, and ensuring fair advertising practices. The results highlight the importance of intelligent automated fraud detection systems in safeguarding the integrity of the online advertising ecosystem.