Detecting Fraudulent Click Patterns in Mobile In-App Browsers: A Multi-dimensional Behavioral Analysis Approach

Abstract

Mobile in-app browsers have become primary channels for digital advertising, processing billions of daily ad impressions. This infrastructure faces escalating threats from sophisticated click fraud operations that exploit behavioral blind spots unique to WebView environments. We present a comprehensive analysis of fraudulent click patterns through multi-dimensional behavioral feature extraction spanning user interaction sequences, device fingerprints, and network-level signals. Our approach characterizes the distinct temporal, spatial, and contextual attributes that differentiate automated fraud from genuine user engagement across 847,293 advertising sessions. The detection framework achieves 94.7% precision and 91.3% recall in identifying coordinated click fraud, traffic hijacking, and ad injection attacks while maintaining privacy-preserving data collection boundaries. Experimental validation demonstrates robustness against evolving evasion techniques and scalability for real-time deployment in production advertising systems serving over 10 million daily active users.