Skip to main content
SpringerLink
Log in

Novel Machine-Learning-Based Decision Support System for Fraud Prevention

  • Conference paper
  • First Online:
5th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2022)

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

  • 106 Accesses

Abstract

Due to the popularity of cloud services, it is unavoidable, that not just legitimate, but fraudulent registrations will happen. For a service with good reputation, it is essential to prevent fraud users. A common way is to filter these cases during the registration process by analysts. This chapter presents a novel decision support system that can recognize anomalous behavioral patterns and classify accounts based on the available data thus implementing an automated fraud prevention system. The process uses both supervised and unsupervised approaches, thus avoiding errors due to inaccurate labeling. As a supervised machine learning algorithm, random forest classifier and logistic regression are used, and as an unsupervised, auto encoder is used. The developed flow gives a recommendation to the analyst whether a new user is potentially fraud or not and provides feedback on the accuracy of analysts’ work based on the results of the unsupervised approach. The newly developed process is able to supervise the decisions made by analysts thus improving the labeling process. The main goal of this chapter is to present a new, more deterministic labeling workflow with the ability to provide feedback so it can improve the correctness of the training data set.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
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

  1. P. Koehler, A. Anandasivam, D. Ma, Cloud services from a consumer perspective, in Proceedings of the 16th Americas Conference on Information Systems (AMCIS 2010), Lima, Peru (2010)

    Google Scholar 

  2. Internet Crime Complaint Centre IC3, Internet Crime Report 2021. Federal Bureau of Investigation (2021)

    Google Scholar 

  3. D.S. Griffith, The Computer Fraud and Abuse Act of 1986: a measured response to a growing problem. Vand. L. Rev. 43, 453 (1990)

    Google Scholar 

  4. L.I. Krauss, A. MacGahan, Computer Fraud and Countermeasures (Prentice-Hall, Englewood Cliffs, 1979)

    Google Scholar 

  5. A.A.Z. Mansour, A. Ahmi, O.M.J. Popoola, A. Znaimat, Discovering the global landscape of fraud detection studies: a bibliometric review. J. Financial Crime 29(2), 701–720 (2022)

    Article  Google Scholar 

  6. Association of Certified Fraud Examiners, Report to the nations on occupational fraud and abuse. Association of Certified Fraud Examiners (2002)

    Google Scholar 

  7. A. Abdallah, M.A. Maarof, A. Zainal, Fraud detection system: a survey. J. Netw. Comput. Appl. 68, 90–113 (2016)

    Article  Google Scholar 

  8. A.O. Hoffmann, C. Birnbrich, The impact of fraud prevention on bank-customer relationships: an empirical investigation in retail banking. Int. J. Bank Marketing 30, 390–407 (2012)

    Article  Google Scholar 

  9. V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)

    Article  Google Scholar 

  10. T. Pourhabibi, K.-L. Ong, B.H. Kam, Y.L. Boo, Fraud detection: a systematic literature review of graph-based anomaly detection approaches. Decision Support Syst. 133, 113303 (2020)

    Article  Google Scholar 

  11. M. Chen, A.A. Ghorbani, et al., A survey on user profiling model for anomaly detection in cyberspace. J. Cyber Security Mob. 8(1), 75–112 (2019)

    Google Scholar 

  12. R. Ramachandran, R. Nidhin, P. Shogil, Anomaly detection in role administered relational databases—a novel method, in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (IEEE, Piscataway, 2018), pp. 1017–1021

    Google Scholar 

  13. A.G. Marakhtanov, E.O. Parenchenkov, N.V. Smirnov, Detection of fictitious accounts registration, in 2021 International Russian Automation Conference (RusAutoCon) (IEEE, Piscataway, 2021), pp. 226–230

    Google Scholar 

  14. B. Sharma, P. Pokharel, B. Joshi, User behavior analytics for anomaly detection using LSTM autoencoder-insider threat detection, in Proceedings of the 11th International Conference on Advances in Information Technology (2020), pp. 1–9

    Google Scholar 

  15. Z. Ding, L. Liu, D. Yu, S. Huang, H. Zhang, K. Liu, Detection of anomaly user behaviors based on deep neural networks, in 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (IEEE, Piscataway, 2021), pp. 1240–1245

    Google Scholar 

  16. V. Hodge, J. Austin, A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)

    Article  MATH  Google Scholar 

  17. O. Alghushairy, R. Alsini, T. Soule, X. Ma, A review of local outlier factor algorithms for outlier detection in big data streams. Big Data Cognit. Comput. 5(1), 1 (2020)

    Google Scholar 

  18. M. Goldstein, S. Uchida, A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLOS ONE 11, 1–31, 04 (2016)

    Google Scholar 

  19. X. Qu, L. Yang, K. Guo, L. Ma, M. Sun, M. Ke, M. Li, A survey on the development of self-organizing maps for unsupervised intrusion detection. Mob. Netw. Appl. 26(2), 808–829 (2021)

    Article  Google Scholar 

  20. S. Omar, A. Ngadi, H.H. Jebur, Machine learning techniques for anomaly detection: an overview. Int. J. Comput. Appl. 79(2), 33–41 (2013)

    Google Scholar 

  21. E. Carrizosa, D.R. Morales, Supervised classification and mathematical optimization. Comput. Oper. Res. 40(1), 150–165 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  22. J.E. Van Engelen, H.H. Hoos, A survey on semi-supervised learning. Mach. Learn. 109(2), 373–440 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  23. C.S. Hilas, J.N. Sahalos, User profiling for fraud detection in telecommunication networks, in 5th International Conference on Technology and Automation (2005), pp. 382–387

    Google Scholar 

  24. T. Lane, C.E. Brodley, An application of machine learning to anomaly detection, in Proceedings of the 20th National Information Systems Security Conference, vol. 377, Baltimore, USA (1997), pp. 366–380

    Google Scholar 

  25. R. Primartha, B.A. Tama, Anomaly detection using random forest: A performance revisited, in 2017 International Conference on Data and Software Engineering (ICoDSE) (IEEE, Piscataway, 2017), pp. 1–6

    Google Scholar 

  26. J. Zhang, M. Zulkernine, A. Haque, Random-forests-based network intrusion detection systems. IEEE Trans. Syst. Man Cyber. Part C (Appl. Rev.) 38(5), 649–659 (2008)

    Google Scholar 

  27. M.A.M. Hasan, M. Nasser, B. Pal, S. Ahmad, Support vector machine and random forest modeling for intrusion detection system (IDS). J. Intell. Learn. Syst. Appl. 2014, 45–52 (2014)

    Google Scholar 

  28. C. Kater, R. Jäschke, You shall not pass: detecting malicious users at registration time, in Proceedings of the 1st International Workshop on Online Safety, Trust and Fraud Prevention (2016), pp. 1–6

    Google Scholar 

  29. K. Kirasich, T. Smith, B. Sadler, Random forest vs logistic regression: binary classification for heterogeneous datasets. SMU Data Sci. Rev. 1(3), 9 (2018)

    Google Scholar 

  30. L. McInnes, J. Healy, J. Melville, UMAP: Uniform manifold approximation and projection for dimension reduction (2018). Preprint arXiv:1802.03426

    Google Scholar 

  31. D.R. Anderson, K.P. Burnham, W.L. Thompson, Null hypothesis testing: problems, prevalence, and an alternative. J. Wildlife Manag. 64, 912–923 (2000)

    Article  Google Scholar 

  32. T.K. Kim, T test as a parametric statistic. Korean J. Anesthesiol. 68(6), 540–546 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The research reported in this paper is part of project no. BME-NVA-02, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021 funding scheme.

Author information

Authors and Affiliations

  1. Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary

    Norman Bereczki, Vilmos Simon & Bernat Wiandt

Authors
  1. Norman Bereczki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vilmos Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bernat Wiandt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Norman Bereczki .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anandakumar Haldorai

  2. Department of Computer Science and Engineering, CMR University, Bengaluru, Karnataka, India

    Arulmurugan Ramu

  3. Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Sudha Mohanram

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bereczki, N., Simon, V., Wiandt, B. (2023). Novel Machine-Learning-Based Decision Support System for Fraud Prevention. In: Haldorai, A., Ramu, A., Mohanram, S. (eds) 5th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. BDCC 2022. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-28324-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28324-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28323-9

  • Online ISBN: 978-3-031-28324-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation