Abstract
This chapter reviews both commercial Ad fraud detection and prevention systems and the ones developed in academia. For commercial systems, they mainly emphasize on the efficiency, so fraud detection can be achieved at pre-auction level (e.g. less than 10 ms). The systems developed in academia are often more sophisticated in their designs and mathematical models. Yet the efficiency of such systems for online usages are often not strictly evaluated.
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References
Areyouahuman (2016) http://areyouahuman.com/
Attenberg J, Ipeirotis P, Provost F (2015) Beat the machine: Challenging humans to find a predictive model’s “unknown unknowns”. Journal of Data and Information Quality (6):1–17
Barnard L, Kreiss D (2013) A research agenda for online political advertising: Surveying campaign practices, 2000–2012. International Journal of Communication (7):2024–2066
Bursztein E, Aigrain J, Moscicki A, Mitchell JC (2014) The end is nigh: Generic solving of text-based captchas. In: 8th USENIX Workshop on Offensive Technologies (WOOT 14), USENIX Association, San Diego, CA, URL https://www.usenix.org/conference/woot14/workshop-program/presentation/bursztein
DoubleVerify D (2016) http://www.doubleverify.com/
Englehardt S, Narayanan A (2016) Online tracking: A 1-million-site measurement and analysis. In: Proceedings the 23rd ACM Conference on Computer and Communications Security
Forensiq (2016) https://forensiq.com/
Ge L, King D, Kantardzic M (2005) Collaborative click fraud detection and prevention system (ccfdp) improves monitoring of software-based click fraud. E-COMMERCE 2005 p 34
Kaminsky D (2015) Detection and prevention of online user interface manipulation via remote control. US Patent App. 14/620,115
Linden J, Teeter T (2012) Method for performing real-time click fraud detection, prevention and reporting for online advertising. US Patent 8,321,269
Liu B, Nath S, Govindan R, Liu J (2014) Decaf: Detecting and characterizing ad fraud in mobile apps. In: 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14), USENIX Association, pp 57–70, URL https://www.usenix.org/conference/nsdi14/technical-sessions/presentation/liu_bin
Moat (2016) https://moat.com/
Science IIA (2016) https://integralads.com/
Validclick (2016) http://validclick.com/
Whiteops (2016) http://www.whiteops.com/
Yu H, Riedl M (2015) Automatic generation of game-base captchas. In: Proceedings of the 2015 Workshop on Procedural Content Generation
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Zhu, X., Tao, H., Wu, Z., Cao, J., Kalish, K., Kayne, J. (2017). Ad Fraud Detection Tools and Systems. In: Fraud Prevention in Online Digital Advertising. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-56793-8_6
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DOI: https://doi.org/10.1007/978-3-319-56793-8_6
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