DETECTIVES: DETEcting Coalition hiT Inflation attacks in adVertising nEtworks Streams
Ahmed Metwally (University of California, Santa Barbara)
Divyakant Agrawal (University of CaliforniaSanta Barbara)
Amr El Abbadi (University of CaliforniaSanta Barbara)
Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of the content publishers' sites. Some publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. We detected numerous coalitions that span numerous sites. Interestingly, 93% of the discovered sites were real fraudsters.