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An Exhaustive Review on Detecting Online Click-Ad Frauds

  • Anurag Srivastav
  • Laxmi Ahuja
Chapter
  • 31 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 103)

Abstract

Social media has become a targeted place for hackers and intruders. The problem is that the detection mechanism which we use is not capable of detecting all the click frauds and has not raised the bar to commit click fraud but is very much effective in the long run. Today’s web browsers support a rich variety of web standards in which a click-bot must be implemented to evade the detection mechanism. A click-bot of heavy size will risk itself of being easily detected by the host. This paper will show a brief review of how this system of detecting fraud-ad works and how we can prevent it from happening with us. This paper reviews what different existing techniques can be used in a more effective way and how they function in a given situation and the ways by which we can keep our data safe from these fraudsters.

Keywords

Fraud-ads Click-bots Detection mechanism JavaScript Machine learning 

Notes

Acknowledgements

Authors express their deep sense of gratitude to the Founder President of Amity University, Dr. Ashok K. Chauhan, for his keen interest in promoting research in the Amity University and have always been an inspiration for achieving great heights.

References

  1. 1.
    Australian Bureau of Statistics (2016) Credit card fraud statistics. AbsGoogle Scholar
  2. 2.
    Daswani N, Stoppelman M (2007) The anatomy of Clickbot.A. In: HotBots’07 proceedings of the first conference on first workshop on hot topics in understanding botnetsGoogle Scholar
  3. 3.
  4. 4.
    Green J Aruba Perspectives with Gartner Report “Seven Imperatives to Adopt a CARTA Approach”, pp 1–24Google Scholar
  5. 5.
    Kintana C, Turner D, Pan J, Metwally A, Daswani N, Chin E, Bortz A (2009) The google ad traffic quality team the goals and challenges of click fraud penetration testing systems. IssreGoogle Scholar
  6. 6.
    Kshetri N, Voas J (2019) Online advertising fraud. Comput (Long Beach Calif) 52:58–61.  https://doi.org/10.1109/MC.2018.2887322CrossRefGoogle Scholar
  7. 7.
    Mungamuru B, Weis S (2008) Competition and fraud in online advertising markets. In: Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics), vol 5143, LNCS, pp 187–191.  https://doi.org/10.1007/978-3-540-85230-8_16
  8. 8.
    Nagaraja S, Shah R (2019) Clicktok: click fraud detection using traffic analysisGoogle Scholar
  9. 9.
    Oentaryo R, Lim E-P, Finegold M, Lo D, Zhu F, Phua C, Sim K, Nguyen MN, Faisal M, Aung Z, Woon WL, Chen W, Berrar D, Cheu E-Y, Yap G-E, Perera K, Neupane B, Patel D, Oentaryo DB (2014) Detecting click fraud in online advertising: a data mining approach Ghim-Eng YapGoogle Scholar
  10. 10.
    The Fraud Practice; Fraud Library—History of Credit Card FraudGoogle Scholar
  11. 11.
    Council Post—How to Reduce Chargebacks Without Killing Online SalesGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Anurag Srivastav
    • 1
  • Laxmi Ahuja
    • 1
  1. 1.Amity Institute of Information TechnologyAmity UniversityNoidaIndia

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