Click fraud prevention in pay-per-click model: Learning through multi-model evidence fusion

Abstract

Multi-sensor data fusion has been an area of intense recent research and development activity. This concept has been applied to numerous fields and new applications are being explored constantly. Multi-sensor based Collaborative Click Fraud Detection and Prevention (CCFDP) system can be viewed as a problem of evidence fusion. In this paper we detail the multi level data fusion mechanism used in CCFDP for real time click fraud detection and prevention. Prevention mechanisms are based on blocking suspicious traffic by IP, referrer, city, country, ISP, etc. Our system maintains an online database of these suspicious parameters. We have tested the system with real-world data from an actual ad campaign where the results show that use of multilevel data fusion improves the quality of click fraud analysis.