Fraud Detection via Coding Nominal Attributes

Research on advertisement has mainly focused on how to accurately predict the click-through rate (CTR). Much less is known about fraud detection and malicious behavior defense. Previous studies usually use statistics, design threshold and manually make strategies, which cannot find potential fraud behavior effectively and suffer from new attacks. In this paper, we make the first step to understand the type of malicious activities on large-scale online advertising platforms. By analyzing each feature comprehensively, we propose a novel coding approach to transform nominal attributes into numeric while maintaining the most effective information of the original data for fraud detection. Next, we code important features such as IP and cookie in our dataset and train machine learning methods to detect fraud traffic automatically. Experimental results on real datasets demonstrate that the proposed fraud detection method performs well considering both the accuracy and efficiency. Finally, we conclude how to design a defense system by considering which methods could be used for the anti-spam gaming in the future.