Click fraud prevention via multimodal evidence fusion by Dempster-Shafer theory

Mehmed Kantardzic, University of Louisville
Chamila Walgampaya, University of Louisville
Roman Yampolskiy, University of Louisville
Ryu Joung Woo, University of Louisville


We address the problem of combining information from diversified sources in a coherent fashion. A generalized evidence processing theory and an architecture for data fusion that accommodates diversified sources of information are presented. Different levels at which data fusion may take place such as the level of dynamics, the level of attributes, and the level of evidence are discussed. A multi-level fusion architecture based Collaborative Click Fraud Detection and Prevention (CCFDP) system for real time click fraud detection and prevention is proposed and its performance is compared with a traditional rule based click fraud detection system. Both systems are tested with real world data from an actual ad campaign. Results show that use of multi-level data fusion improves the quality of click fraud analysis. © 2010 IEEE.