Cache Cookies for Browser Authentication (Extended Abstract)
SP '06 Proceedings of the 2006 IEEE Symposium on Security and Privacy
Fighting online click-fraud using bluff ads
ACM SIGCOMM Computer Communication Review
An effective method for combating malicious scripts clickbots
ESORICS'09 Proceedings of the 14th European conference on Research in computer security
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ACNS'10 Proceedings of the 8th international conference on Applied cryptography and network security
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Proceedings of the 8th USENIX conference on Networked systems design and implementation
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DIMVA'11 Proceedings of the 8th international conference on Detection of intrusions and malware, and vulnerability assessment
Understanding fraudulent activities in online ad exchanges
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Measuring and fingerprinting click-spam in ad networks
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
AdSplit: separating smartphone advertising from applications
Security'12 Proceedings of the 21st USENIX conference on Security symposium
Measuring and fingerprinting click-spam in ad networks
ACM SIGCOMM Computer Communication Review - Special october issue SIGCOMM '12
Duplicate detection in pay-per-click streams using temporal stateful Bloom filters
International Journal of Data Analysis Techniques and Strategies
ViceROI: catching click-spam in search ad networks
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
DECAF: detecting and characterizing ad fraud in mobile apps
NSDI'14 Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation
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We propose new techniques to combat the problem of click fraud in pay-per-click (PPC) systems. Rather than adopting the common approach of filtering out seemingly fraudulent clicks, we consider instead an affirmative approach that only accepts legitimate clicks, namely those validated through client authentication. Our system supports a new advertising model in which "premium" validated clicks assume higher value than ordinary clicks of more uncertain authenticity. Click validation in our system relies upon sites sharing evidence of the legitimacy of users (distinguishing them from bots, scripts, or fraudsters). As cross-site user tracking raises privacy concerns among many users, we propose ways to make the process of authentication anonymous. Our premium-click scheme is transparent to users. It requires no client-side changes and imposes minimal overhead on participating Web sites.