Abstract
From the viewpoint of Dempster-Shafer evidence theory, information obtained from different sources can be considered as pieces of evidence, and as such, multi-sensor based CCFDP (Collaborative Click Fraud Detection and Prevention) system can be viewed as a problem of evidence fusion. In this paper we detail the multi level data fusion mechanism used in CCFDP for real time click fraud detection and prevention. Prevention mechanisms are based on blocking suspicious traffic by IP, referrer, city, country, ISP, etc. Our system maintains an online database of these suspicious parameters. We have tested the system with real world data from an actual ad campaign where the results show that use of multi-level data fusion improves the quality of click fraud analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Benaskeur AR, Rheaume F (2007) Adaptive data fusion and sensor management for military applications. Aerosp Sci Technol 11(4):327–338
Cai D, McTear M, McClean S (2000) Knowledge discovery in distributed databases using evidence theory. Int J Intell Syst 15(8):745–761
Carvalho H, Heinzelman W, Murphy A, Coelho C (2003)“A general data fusion architecture”. In: International conference on information fusion, Cairns, Queensland, Australia, pp 1465–1472
Clerentin A, Delahoche L, Brassart E (2000) “Cooperation between two omnidirectional perception systems for mobile robot localization”. In: Proceedings of the 2000 IEEE/RSJ intemational conference on intelligent robots and systems, Takamatsu, Japan, pp 1499–1504
Dai X, Khorram S (1999) Data fusion using artificial neural networks: a case study on multitemporal change analysis. Comput Environ Urban Syst 23(1):19–31
Durrant-Whyte H (1987) “Integration, coordination and control of multi-sensor robot systems”. Dissertation Abstracts International 47(10)
Ge L, Kantardzic M (2006) Real-time click fraud detecting and blocking system. US Patent App. 11/413,983, 1 May 2006
Hager G, Engelson S, Atiya S (1993) “On comparing statistical and set-based methods in sensor data fusion”. In: IEEE international conference on robot automation. Atlanta, USA
Han J, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco
Immorlica N, Jain K, Mahdian M, Talwar K (2005) Click fraud resistant methods for learning click-through rates. Lecture Notes in Computer Science 3828:34–45
Janez F, Goretta O, Michel A (2000) “Automatic map updating by fusion of multispectral images in the Dempster-Shafer framework”. In: Proceedings of SPIE, vol 4115. p 245
Kantardzic M, Walgampaya C, Wenerstrom B, Lozitskiy O, Higgins S, King D (2008) “Improving click fraud detection by real time data fusion”. In: IEEE international symposium on signal processing and information technology, 2008. ISSPIT 2008, pp 69–74
Kantardzic M, Wenerstrom B, Walgampaya C, Lozitskiy O, Higgins S, King D (2009) “Time and space contextual information improves click quality estimation,” e-Commerce 2009, p 123
Lambert D (2009) A blueprint for higher-level fusion systems. Inf Fusion 10(1):6–24
Mahdian M (2006) “Theoretical challenges in the design of advertisement auctions”. In: The capital area theory symposia, University of Maryland, Spring
Metwally A, Agrawal D, El Abbadi A. (2007) “Detectives: detecting coalition hit inflation attacks in advertising networks streams”. In: Proceedings of the 16th international conference on World Wide Web, ACM,
NetMosaics (2009) NetMosaics Inc. internal documentation
Ouyang N, Liu Z, Kang H (2008)“A method of distributed decision fusion based on SVM and DS evidence theory”. In: 5th international conference on visual information engineering, pp 261–264
Shafer G (1976) A mathematical theory of evidence. Princeton university press, Princeton
Solaiman B, Pierce L, Ulaby F (1999) Multisensor data fusion using fuzzy concepts: application to land-cover classification using ERS-1/JERS-1 SAR composites. IEEE Trans Geosci Remote Sens 37(3):1316–1326
Tian J, Zhao W, Du R, Zhang Z (2005) “DS evidence theory and its data fusion application in intrusion detection”. In: Lecture notes in computer science, 3802
Wald L (2001)“The present achievements of the EARSeL-SIG’data fusion, in a decade of trans-european remote sensing cooperation”. In: Proceedings of the 20th Earsel Symposium, Taylor & Francis, Dresden, 14–16 June 2000, p 263
Walgampaya C, Kantardzic M, Yampolskiy R (2010) “Real time click fraud prevention using multi-level data fusion, lecture notes in engineering and computer science. In: proceedings of the world congress on engineering and computer science 2010,” WCECS 2010, 20–22 Oct 2010, San Francisco pp 514–519
Waltz E (1998) “Information understanding: integrating data fusion and data mining processes”. In: IEEE international symposium on circuits and systems, pp 553–556
Wu H, Siegel M, Stiefelhagen R, Yang J (2002) “Sensor fusion using Dempster-Shafer theory”. In: IEEE instrumentation and measurement technology conference proceedings, vol 1. pp 7–12
Yukun C, Xicai S, Zhigang L (2007) Research on Kalman-filter based multisensor data fusion. J Syst Eng Electron 18(3):497–502
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Walgampaya, C., Kantardzic, M., Yampolskiy, R. (2011). Evidence Fusion for Real Time Click Fraud Detection and Prevention. In: Ao, SI., Amouzegar, M., Rieger, B. (eds) Intelligent Automation and Systems Engineering. Lecture Notes in Electrical Engineering, vol 103. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0373-9_1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-0373-9_1
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-0372-2
Online ISBN: 978-1-4614-0373-9
eBook Packages: EngineeringEngineering (R0)