Improving Click Fraud Detection by Real Time Data Fusion

Abstract

Click fraud is a type of Internet crime that occurs in pay per click online advertising when a person, automated script, or computer program imitates a legitimate user of a Web browser clicking on an ad, for the purpose of generating a charge per click without having actual interest in the target of the ad's link. Most of the available commercial solutions are just click fraud reporting systems, not real-time click fraud detection and prevention systems. A new solution is proposed in this paper that will analyze the detailed user click activities based on data collected form different sources. More information about each click enables better evaluation of the quality of click traffic. We utilize the multi source data fusion to merge client side and server side activities. Proposed solution is integrated in our CCFDP V1.0 system for a real-time detection and prevention of click fraud. We have tested the system with real world data from an actual ad campaign where the results show that additional real-time information about clicks improve the quality of click fraud analysis.