Skip to main content

Evidence Fusion for Real Time Click Fraud Detection and Prevention

  • Chapter
  • First Online:
Intelligent Automation and Systems Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 103))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Benaskeur AR, Rheaume F (2007) Adaptive data fusion and sensor management for military applications. Aerosp Sci Technol 11(4):327–338

    Article  MATH  Google Scholar 

  • Cai D, McTear M, McClean S (2000) Knowledge discovery in distributed databases using evidence theory. Int J Intell Syst 15(8):745–761

    Article  MATH  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Durrant-Whyte H (1987) “Integration, coordination and control of multi-sensor robot systems”. Dissertation Abstracts International 47(10)

    Google Scholar 

  • Ge L, Kantardzic M (2006) Real-time click fraud detecting and blocking system. US Patent App. 11/413,983, 1 May 2006

    Google Scholar 

  • 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

    Google Scholar 

  • Han J, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Lambert D (2009) A blueprint for higher-level fusion systems. Inf Fusion 10(1):6–24

    Article  Google Scholar 

  • Mahdian M (2006) “Theoretical challenges in the design of advertisement auctions”. In: The capital area theory symposia, University of Maryland, Spring

    Google Scholar 

  • 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,

    Google Scholar 

  • NetMosaics (2009) NetMosaics Inc. internal documentation

    Google Scholar 

  • 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

    Google Scholar 

  • Shafer G (1976) A mathematical theory of evidence. Princeton university press, Princeton

    MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Waltz E (1998) “Information understanding: integrating data fusion and data mining processes”. In: IEEE international symposium on circuits and systems, pp 553–556

    Google Scholar 

  • 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

    Google Scholar 

  • Yukun C, Xicai S, Zhigang L (2007) Research on Kalman-filter based multisensor data fusion. J Syst Eng Electron 18(3):497–502

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chamila Walgampaya .

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics