Publisher-Centric Machine Learning-Based Solution for Click Fraud

Abstract

Invalid traffic and click fraud present significant challenges in online advertising, impacting advertising metrics and causing substantial financial losses across the digital advertising ecosystem. While advertisers have access to various protective solutions and receive protection from advertising networks, publishers face limited options for detecting and preventing fraudulent activities on their websites. This gap in publisher-side protection creates a critical area for investigation and development of practical solutions. This research presents an effective publisher-side solution: the Ad Click Fraud Protector (ACFP), an open-source WordPress plugin that detects and prevents click fraud and invalid traffic. The research methodology involved studying browser fingerprinting approaches by collecting browser fingerprints from legitimate users and bots, distinguished through firewall rules and honeypots. Experimental analysis identified six key browser fingerprinting attributes that effectively distinguish between legitimate and fraudulent traffic. These findings informed the development of the ACFP plugin, which incorporates additional security measures for enhanced protection. Testing of the plugin on two AdSense publisher accounts demonstrated its effectiveness in reducing invalid clicks, minimizing invalid traffic, and decreasing revenue deductions due to invalid clicks. The results show that publishers can effectively protect their ad accounts from penalties and deductions through browser fingerprint-based traffic filtering. This research provides publishers with an accessible, opensource solution for combating click fraud while contributing to the theoretical understanding of browser fingerprinting effectiveness in fraud detection. Additionally, it establishes a framework for future development in publisher-side protection systems.