Why go logarithmic if we can go linear?: Towards effective distinct counting of search traffic
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
SLEUTH: Single-pubLisher attack dEtection Using correlaTion Hunting
Proceedings of the VLDB Endowment
Thread cooperation in multicore architectures for frequency counting over multiple data streams
Proceedings of the VLDB Endowment
SBotMiner: large scale search bot detection
Proceedings of the third ACM international conference on Web search and data mining
SpotRank: a robust voting system for social news websites
Proceedings of the 4th workshop on Information credibility
Peeking through the cloud: DNS-based estimation and its applications
ACNS'08 Proceedings of the 6th international conference on Applied cryptography and network security
An effective method for combating malicious scripts clickbots
ESORICS'09 Proceedings of the 14th European conference on Research in computer security
Peeking Through the Cloud: Client Density Estimation via DNS Cache Probing
ACM Transactions on Internet Technology (TOIT)
The dark side of the Internet: Attacks, costs and responses
Information Systems
Measuring and fingerprinting click-spam in ad networks
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
Measuring and fingerprinting click-spam in ad networks
ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12
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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 ecommerce. 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. 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 using online algorithms on aggregate data to accurately and proactively detect automated traffic, preserve surfers' privacy, 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.