ADAPTIVE HYBRID FRAMEWORK FOR REAL TIME AD CLICK FRAUD DETECTION

The growth of digital advertising has significantly increased business opportunities, but it has also introduced challenges such as click fraud. Click fraud occurs when bots or malicious users generate fake clicks on online advertisements, resulting in financial loss for advertisers and inaccurate campaign performance metrics. Traditional detection methods are often unable to handle evolving fraud patterns and large-scale real-time data. This project proposes an Adaptive Hybrid Framework for Real-Time Ad Click Fraud Detection that integrates machine learning and deep learning techniques. The system analyzes user behavior, session attributes, and temporal patterns to distinguish between legitimate and fraudulent clicks. Tree-based machine learning models efficiently detect suspicious activities, while LSTM-based deep learning models capture sequential user behavior. The hybrid approach improves detection accuracy, reduces false negatives, and ensures real-time monitoring. The proposed framework provides a scalable and adaptive solution to enhance the reliability and security of online advertising platforms.