Abbadi. On Hit Inflation Techniques and Detection in Streams of Web Advertising Networks

Click fraud is jeopardizing the industry of Internet ad-vertising. 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 dishon-est, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their com-petitors ’ advertising budgets. In this paper, we describe the advertising network model, and discuss the issue of fraud that is an integral problem in such setting. We propose us-ing online algorithms on aggregate data to accurately and proactively detect automated traffic, preserve surfers ’ pri-vacy, while not altering the industry model. We provide a complete classification of the hit inflation techniques; and devise stream analysis techniques that detect a variety of fraud attacks. We abstract detecting the fraud attacks of some classes as theoretical stream analysis problems that we bring to the data management research community as open problems. A framework is outlined for deploying the proposed detection algorithms on a generic architecture. We conclude by some successful preliminary findings of our attempt to detect fraud on a real network. 1