Deep Learning-Based Click Fraud Detection in Mobile Advertising: A Multi-Dimensional Behavioral Feature Analysis Framework

Abstract

Mobile advertising fraud has emerged as a critical challenge in digital marketing ecosystems, causing substantial financial losses and compromising campaign effectiveness. This research presents a comprehensive deep learning framework for detecting fraudulent click patterns in mobile advertising environments through multi-dimensional behavioral analysis. The proposed methodology integrates temporal feature extraction, user interaction pattern recognition, and anomaly detection algorithms to distinguish legitimate user engagement from automated bot activities and coordinated fraud schemes. Experimental evaluations conducted on real-world mobile advertising datasets demonstrate detection accuracy exceeding 94.7% while maintaining false positive rates below 2.3%. The framework incorporates adaptive threshold mechanisms that adjust to evolving fraud tactics and provides interpretable risk scores for advertising platforms. Performance benchmarking against conventional rule-based and traditional machine learning approaches reveals substantial improvements in both detection precision and computational efficiency. The research contributes practical insights for securing mobile advertising infrastructure and protecting marketing investments from fraudulent manipulation.