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Data Analysis Algorithm for Click Fraud Recognition

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Information and Software Technologies (ICIST 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 920))

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Abstract

This paper presents an analytical system designed to detect click fraud on the Internet. The algorithm works with the data collected from an advertiser’s website to which the Pay-Per-Click traffic is directed. This traffic is not entirely carried out by humans, as a large part of it is carried out by bots – software running automated tasks. The purpose of the proposed algorithm is to analyze the data of individual clicks coming from advertisements and to automatically classify them as suspicious or correct. The paper presents the mechanisms of comparing different types of data, their classification and the tuning of particular elements of the algorithm. Results of the experimental research confirming the effectiveness of the proposed methods are also presented.

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References

  1. Salton, G., Buckley, C.: Term-weighing approaches sin automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)

    Article  Google Scholar 

  2. Ramos, J.J.: Using TF-IDF to determine word relevance in document queries. Technical report, Department of Computer Science. Rutgers University (2000)

    Google Scholar 

  3. http://blog.pixalate.com/desktop-ad-click-fraud-rising-stats-data-2017. Accessed 13 June 2018

  4. https://github.com/Valve/fingerprintjs2. Accessed 13 June 2018

  5. https://support.google.com/adwords/answer/42995?hl=en. Accessed 13 June 2018

  6. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  7. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)

    Article  Google Scholar 

  8. Olson, D.L., Delen, D.: Advanced Data Mining Techniques, 1st edn. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  9. Neal, A., Kouwenhoven, S., Sa, O.: Quantifying online advertising fraud: Ad-click bots vs humans. Technical report, Oxford Bio Chronometrics (2015)

    Google Scholar 

  10. Zhu, X., et al.: Fraud Prevention in Online Digital Advertising. Springer, Cham (2017)

    Book  Google Scholar 

  11. Seyyar, M.B., Çatak, F.Ö., Gül, E.: Detection of attack-targeted scans from the Apache HTTP Server access logs. Appl. Comput. Inf. 14(1) (2018)

    Google Scholar 

  12. AsSadhan, B., Moura, J., Lapsley, D., Jones, C., Strayer, W.: Detecting botnets using command and control traffic. In: Eighth IEEE International Symposium on Network Computing and Applications, NCA 2009, pp. 156–162 (2009)

    Google Scholar 

  13. Woźniak, M., Połap, D.: Bio-inspired methods modeled for respiratory disease detection from medical images. Swarm Evol. Comput. 41, 69–96 (2018). https://doi.org/10.1016/j.swevo.2018.01.008

    Article  Google Scholar 

  14. Woźniak, M., Połap, D.: Adaptive neuro-heuristic hybrid model for fruit peel defects detection. Neural Netw. 98, 16–33 (2018). https://doi.org/10.1016/j.neunet.2017.10.009

    Article  Google Scholar 

  15. Iqbal, M.S., Zulkernine, M., Jaafar, F., Gu, Y.: Protecting internet users from becoming victimized attackers of click-fraud. J. Softw. Evol. Process 30(3), e1871 (2018)

    Article  Google Scholar 

  16. Lee, S.C., Faloutsos, C., Chae, D.K., Kim, S.W.: Fraud detection in comparison-shopping services: patterns and anomalies in user click behaviors. IEICE Trans. Inf. Syst. 100(10), 2659–2663 (2017)

    Article  Google Scholar 

  17. Damasevicius, R., Valys, R., Wozniak, M.: Intelligent tagging of online texts using fuzzy logic. In: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (2017). https://doi.org/10.1109/ssci.2016.7849917

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Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Marcin Gabryel

  2. Spark DigitUp Sp. z. o. o., Plac Wolnica 13 lok. 10, 31-060, Kraków, Poland

    Marcin Gabryel

Authors
  1. Marcin Gabryel
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Corresponding author

Correspondence to Marcin Gabryel .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Giedrė Vasiljevienė

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Gabryel, M. (2018). Data Analysis Algorithm for Click Fraud Recognition. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_36

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  • DOI: https://doi.org/10.1007/978-3-319-99972-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99971-5

  • Online ISBN: 978-3-319-99972-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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