Multi-Stage Malicious Click Detection on Large Scale Web Advertising Data

The healthy development of the Internet largely depends on the online advertisement which provides the financial support to the Internet. Click fraud, however, poses serious threat to the Internet ecosystem. It not only brings harm to the advertisers, but also damages the mutual trust between advertiser and ad agency. Click fraud prediction is a typical big data application in that we normally need to identify the malicious clicks from massive click logs, therefore e cient detection methods in big data framework are much desired to combat this fraudulent behavior. In this paper, we propose a three-stage filtering system to attack click fraud. The serialized filters e↵ectively detect the malicious clicks with decreasing confidence that can satisfy both advertisers and content providers.