Feature-Based Detection of Bot Traffic and Click Fraud in Mobile Advertising: A Comparative Analysis

Abstract

Mobile advertising fraud has emerged as a critical security challenge, causing substantial financial losses across digital ecosystems. This paper presents a comprehensive comparative analysis of machine learning algorithms for detecting bot traffic and click fraud through feature-based approaches. We engineer and evaluate temporal, behavioral, and device-specific features across multiple classification algorithms including Random Forest, XGBoost, LightGBM, and deep learning architectures. Experimental results on real-world advertising datasets demonstrate that ensemble methods achieve superior performance with accuracy exceeding 98%, while deep learning approaches provide enhanced robustness against sophisticated fraud patterns. Feature importance analysis reveals that temporal activity patterns and device consistency metrics serve as primary discriminators between legitimate and fraudulent traffic. Our findings provide actionable deployment guidelines for advertising platforms balancing detection accuracy with computational efficiency.