SBotMiner: large scale search bot detection
Proceedings of the third ACM international conference on Web search and data mining
An effective method for combating malicious scripts clickbots
ESORICS'09 Proceedings of the 14th European conference on Research in computer security
A hybrid method to detect deflation fraud in cost-per-action online advertising
ACNS'10 Proceedings of the 8th international conference on Applied cryptography and network security
Space-efficient tracking of persistent items in a massive data stream
Proceedings of the 5th ACM international conference on Distributed event-based system
Finding critical thresholds for defining bursts
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Learning to target: what works for behavioral targeting
Proceedings of the 20th ACM international conference on Information and knowledge management
Understanding fraudulent activities in online ad exchanges
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Finding the right consumer: optimizing for conversion in display advertising campaigns
Proceedings of the fifth ACM international conference on Web search and data mining
Web-scale user modeling for targeting
Proceedings of the 21st international conference companion on World Wide Web
Approximate membership query over time-decaying windows for event stream processing
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Duplicate detection in pay-per-click streams using temporal stateful Bloom filters
International Journal of Data Analysis Techniques and Strategies
Inferential time-decaying Bloom filters
Proceedings of the 16th International Conference on Extending Database Technology
Towards a robust modeling of temporal interest change patterns for behavioral targeting
Proceedings of the 22nd international conference on World Wide Web
Impression fraud in online advertising via pay-per-view networks
SEC'13 Proceedings of the 22nd USENIX conference on Security
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With the rapid growth of the Internet, online advertisement plays a more and more important role in the advertising market. One of the current and widely used revenue models for online advertising involves charging for each click based on the popularity of keywords and the number of competing advertisers. This pay-per-click model leaves room for individuals or rival companies to generate false clicks (i.e., click fraud), which pose serious problems to the development of healthy online advertising market. To detect click fraud, an important issue is to detect duplicate clicks over decaying window models, such as jumping windows and sliding windows. Decaying window models can be very helpful in defining and determining click fraud. However, although there are available algorithms to detect duplicates, there is still a lack of practical and effective solutions to detect click fraud in pay-per-click streams over decaying window models. In this paper, we address the problem of detecting duplicate clicks in pay-per-click streams over jumping windows and sliding windows, and are the first that propose two innovative algorithms that make only one pass over click streams and require significantly less memory space and operations. GBF algorithm is built on group Bloom filters which can process click streams over jumping windows with small number of sub-windows, while TBF algorithm is based on a new data structure called timing Bloom filter that detects click fraud over sliding windows and jumping windows with large number of sub-windows. Both GBF algorithm and TBF algorithm have zero false negative. Furthermore, both theoretical analysis and experimental results show that our algorithms can achieve low false positive rate when detecting duplicate clicks in pay-per-click streams over jumping windows and sliding windows.