Skip to main content
SpringerLink
Log in

AI-JasCon: An Artificial Intelligent Containerization System for Bayesian Fraud Determination in Complex Networks

  • Chapter
  • First Online:
Artificial Intelligence for Cloud and Edge Computing

Part of the book series: Internet of Things ((ITTCC))

  • 1135 Accesses

Abstract

The post-COVID-19 era will create major financial losses in organizational resources as a result of fraudulent activities by malicious agents existing at the edge and cloud domains. Most transactional systems from the edge-to-cloud layers lack the robust platform integration (such as application program interface (API) microservices) needed for fraud mitigation in networks and systems. This paper presents an AI containerization API system based on JAVA-SQL container (JSR-233) for fraud prediction and prevention in telecommunication networks. Pipeline modeling involving the Bayesian software implementation paradigm is introduced using the AI-JasCon model. A demonstration of how the AI engine works with a complex network system for observation of some calls, call frequency, and hidden activities for predictive classification (analytics) at the network backend is discussed. Robust network architecture is introduced for deterministic data mining while creating Bayesian computation to determine fraud potentials through prior, posterior, and joint probability distributions. AI-JasCon framework achieves predictive fraud detection with containerization and modularization using class models and data structures. At the network core layer, an enterprise management backend uses linear discriminant via fog controllers that processes identified fraud subscribers in the network. Also, a standard Java middleware container for distributed transaction management, directory services, and messaging is used to test the application. AI-JasCon framework provides a successful standard for determining fraudulent interactions in edge-to-cloud networks while providing a pipeline application programming model for continuous integration and continuous delivery (CI/CD).

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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

Similar content being viewed by others

References

  1. Zhou C, Lin Z (2018) Study on fraud detection of telecom industry based on rough set. In: IEEE 8th annual computing and communication workshop and conference (CCWC), 8–10 Jan 2018, Las Vegas, NV, USA. https://doi.org/10.1109/CCWC.2018.8301651

    Chapter  Google Scholar 

  2. DuHadway S, Talluri S, Ho W, Buckhoff T (2020) Light in dark places: the hidden world of supply chain fraud. IEEE Trans Eng Manag:1–14. https://doi.org/10.1109/TEM.2019.2957439

  3. de Araujo B, de Almeida H, de Mello F (2019) Computational intelligence methods applied to the fraud detection of electric energy consumers. IEEE Lat Am Trans 17(01):71–77. https://doi.org/10.1109/TLA.2019.8826697

    Article  Google Scholar 

  4. Yashchin E (2007) Modeling of risk losses using size-biased data. IBM J Res Dev 51(3.4):309–323. https://doi.org/10.1147/rd.513.0309

    Article  Google Scholar 

  5. Almehmadi A, El-Khatib K (2017) On the possibility of insider threat prevention using intent-based access control (IBAC). IEEE Syst J 11(2):373–384. https://doi.org/10.1109/JSYST.2015.2424677

    Article  Google Scholar 

  6. The Hindu Technology (2020) Cybercrime could cost the world almost $1 trillion in 2020, McAfee says – The Hindu. Online URL https://www.thehindu.com/sci-tech/technology/cybercrime-could-cost-the-world-almost-1-trillion/article33269047.ece

  7. Allodi L, Hutchings A, Massacci F, Pastrana S, Vasek M (2020) WACCO 2020: the 2nd workshop on attackers and cybercrime operations co-held with IEEE European symposium on security and privacy 2020. In: 2020 IEEE European symposium on security and privacy workshops (EuroS&PW), Genoa, Italy, pp 427–427. https://doi.org/10.1109/EuroSPW51379.2020.00063

    Chapter  Google Scholar 

  8. Wang C, Zhu H (2020) Representing fine-grained co-occurrences for behavior-based fraud detection in online payment services. IEEE Trans Dependable Secure Comput. https://doi.org/10.1109/TDSC.2020.2991872

  9. Saheed YK, Hambali MA, Arowolo MO, Olasupo YA (2020) Application of GA feature selection on naive Bayes, random forest and SVM for credit card fraud detection. In: 2020 international conference on decision aid sciences and application (DASA), Sakheer, Bahrain, pp 1091–1097. https://doi.org/10.1109/DASA51403.2020.9317228

    Chapter  Google Scholar 

  10. Jia X, Yang J, Liu R, Wang X, Cotofana SD, Zhao W (2020) Efficient computation reduction in Bayesian neural networks through feature decomposition and memorization. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2987760

  11. Ye Q, Amini A, Zhou Q (2020) Optimizing regularized Cholesky score for order-based learning of Bayesian networks. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2020.2990820

  12. Posch K, Pilz J (2020) Correlated parameters to accurately measure uncertainty in deep neural networks. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2980004

  13. Yan S, Yao K, Tran CC (2021) Using artificial neural network for predicting and evaluating situation awareness of operator. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3055345

  14. Chiu C, Hsu P-L (2005) A constraint-based genetic algorithm approach for mining classification rules. IEEE Trans Syst Man Cybern Part C Appl Rev 35(2):205–220. https://doi.org/10.1109/TSMCC.2004.841919

    Article  Google Scholar 

  15. Wang S, Cao L (2020) Inferring implicit rules by learning explicit and hidden item dependency. IEEE Trans Syst Man Cybern Syst 50(3):935–946. https://doi.org/10.1109/TSMC.2017.2768547

    Article  Google Scholar 

  16. Zhang D, Zhou L (2004) Discovering golden nuggets: data mining in financial application. IEEE Trans Syst Man Cybern Part C Appl Rev 34(4):513–522. https://doi.org/10.1109/TSMCC.2004.829279

    Article  Google Scholar 

  17. Díaz Vera JC, Negrín Ortiz GM, Molina C, Vila MA (2019) Extending knowledge based redundancy in association rules with imprecise knowledge. IEEE Lat Am Trans 17(04):648–653. https://doi.org/10.1109/TLA.2019.8891930

    Article  Google Scholar 

  18. Charte F, Rivera AJ, del Jesus MJ, Herrera F (2014) LI-MLC: a label inference methodology for addressing high dimensionality in the label space for multilabel classification. IEEE Trans Neural Netw Learn Syst 25(10):1842–1854. https://doi.org/10.1109/TNNLS.2013.2296501

    Article  Google Scholar 

  19. Mahmoud MS, Khan GD (2018) LMI consensus condition for discrete-time multi-agent systems. IEEE/CAA J Autom Sin 5(2):509–513. https://doi.org/10.1109/JAS.2016.7510016

    Article  MathSciNet  Google Scholar 

  20. Li H, Li X (2020) Distributed model predictive consensus of heterogeneous time-varying multi-agent systems: with and without self-triggered mechanism. IEEE Trans Circuits Syst I Regul Pap 67(12):5358–5368. https://doi.org/10.1109/TCSI.2020.3008528

    Article  MathSciNet  MATH  Google Scholar 

  21. Shi J (2020) Cooperative control for nonlinear multi-agent systems based on event-triggered scheme. IEEE Trans Circuits Syst II Express Briefs. https://doi.org/10.1109/TCSII.2020.3035075

  22. Zhou C, Lin Z (2018) Study on fraud detection of telecom industry based on rough set. In: Proceedings of the IEEE 8th annual computing and communication workshop and conference (CCWC), 8–10 Jan. 2018, Las Vegas, NV, USA. https://doi.org/10.1109/CCWC.2018.8301651

    Chapter  Google Scholar 

  23. Gopal RK, Meher SK (2007) A rule-based approach for anomaly detection in subscriber usage pattern. Int J Eng Appl Sci 3:7

    Google Scholar 

  24. Estévez PA, Held CM, Perez CA (2006) Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Syst Appl 31:337–344

    Article  Google Scholar 

  25. Awoyelu L, Adebomi A (2015) Fraud detection in telecommunications industry: bridging the gap with random rough subspace based neural network ensemble method. Comput Eng Intell Syst 6(10)

    Google Scholar 

  26. Dong F, Wang H, Li L, Guo Y, Bissyande TF, Liu T, Xu G, Klein J (2018) FraudDroid: automated ad fraud detection for android apps. In: Conference’17, July 2017, Washington, DC, USA 2018. ACM. ISBN 123-4567-24-567/08/06. https://doi.org/10.475/123v4. arXiv:1709.01213v4 [cs.CR] https://doi.org/10.1145/3236024.3236045

    Google Scholar 

  27. Yusoff MIM, Mohamed I, Bakar MRA (2013) Fraud detection in telecommunication industry using Gaussian mixed model. In: Proceedings of international conference on research and innovation in information systems (ICRIIS), 27–28 Nov. 2013, Kuala Lumpur, Malaysia. https://doi.org/10.1109/ICRIIS.2013.6716681

    Chapter  Google Scholar 

  28. Filippov V, Mukhanov L, Shchukin B (2008) Credit card fraud detection system. In: Proceedings of the 7th IEEE international conference on cybernetic intelligent systems, 9–10 Sept. 2008, London, UK. https://doi.org/10.1109/UKRICIS.2008.4798919

    Chapter  Google Scholar 

  29. Niu K, Jiao H, Deng N, Gao Z (2016) A real-time fraud detection algorithm based on intelligent scoring for the telecom industry. In: Proceedings of the international conference on networking and network applications (NaNA), 23–25 July 2016, Hakodate, Japan. https://doi.org/10.1109/NaNA.2016.11

    Chapter  Google Scholar 

  30. Tarmazakov EI, Silnov DS (2018) Modern approaches to prevent fraud in mobile communications networks. In: Proceedings of the IEEE conference of Russian young researchers in electrical and electronic engineering (EIConRus), 29th Jan.–1st Feb. 2018, Moscow, Russia. https://doi.org/10.1109/EIConRus.2018.8317111

    Chapter  Google Scholar 

  31. Hines C, Youssef A (2018) Machine learning applied to rotating check fraud detection. In: IEEE proceedings of the 2018 1st international conference on data intelligence and security (ICDIS), 8–10 April 2018, South Padre Island, TX, USA. https://doi.org/10.1109/ICDIS.2018.00012

    Chapter  Google Scholar 

  32. Cao C, Gao Y, Luo Y, Xia M, Dong W, Chen C, Liu X (2020) AdSherlock: efficient and deployable click fraud detection for Mobile applications. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2020.2966991

  33. Cheng D, Wang X, Zhang Y, Zhang L (2020) Graph neural network for fraud detection via spatial-temporal attention. IEEE Trans Knowl Data Eng:1–1. https://doi.org/10.1109/TKDE.2020.3025588

  34. Wang C, Wang C, Zhu H, Cui J (2020) LAW: learning automatic windows for online payment fraud detection. IEEE Trans Dependable Secure Comput:1–1. https://doi.org/10.1109/TDSC.2020.3037784

  35. Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G (2018) Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE Trans Neural Netw Learn Syst 29(8):3784–3797. https://doi.org/10.1109/TNNLS.2017.2736643

    Article  Google Scholar 

  36. Ji S, Li J, Yuan Q, Lu J (2020) Multi-range gated graph neural network for telecommunication fraud detection. In: 2020 IEEE international joint conference on neural networks (IJCNN), Glasgow, United Kingdom, pp 1–6. https://doi.org/10.1109/IJCNN48605.2020.9207589

    Chapter  Google Scholar 

  37. Tarmazakov EI, Silnov DS (2018) Modern approaches to prevent fraud in mobile communications networks. In: 2018 IEEE conference of Russian young researchers in electrical and electronic engineering (EIConRus), Moscow, pp 379–381. https://doi.org/10.1109/EIConRus.2018.8317111

    Chapter  Google Scholar 

  38. Mohamed A, Bandi AFM, Tamrin AR, Jaafar MD, Hasan S, Jusof F (2009) Telecommunication fraud prediction using backpropagation neural network. In: 2009 International conference of soft computing and pattern recognition, Malacca, pp 259–265. https://doi.org/10.1109/SoCPaR.2009.60

    Chapter  Google Scholar 

  39. Baharim KN, Kamaruddin MS, Jusof F (2008) Leveraging missing values in call detail record using Naïve Bayes for fraud analysis. In: 2008 International conference on information networking, Busan, pp 1–5. https://doi.org/10.1109/ICOIN.2008.4472791

    Chapter  Google Scholar 

  40. Elrajubi OM, Elshawesh AM, Abuzaraida MA (2017) Detection of bypass fraud based on speaker recognition. In: 2017 8th International conference on information technology (ICIT), Amman, pp 50–54. https://doi.org/10.1109/ICITECH.2017.8079914

    Chapter  Google Scholar 

  41. Xu W, Pang Y, Ma J, Wang S-Y, Hao G, Zeng S, Qian Y-H (2008) Fraud detection in telecommunication: a rough fuzzy set based approach. In: 2008 International conference on machine learning and cybernetics, Kunming, pp 1249–1253. https://doi.org/10.1109/ICMLC.2008.4620596

    Chapter  Google Scholar 

  42. Arafat M, Qusef A, Sammour G (2019) Detection of Wangiri telecommunication fraud using ensemble learning. In: 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT), Amman, Jordan, pp 330–335. https://doi.org/10.1109/JEEIT.2019.8717528

    Chapter  Google Scholar 

  43. Özlan B, Haznedaroğlu A, Arslan LM (2019) Automatic fraud detection in call center conversations. In: 2019 IEEE 27th signal processing and communications applications conference (SIU), Sivas, Turkey, pp 1–4. https://doi.org/10.1109/SIU.2019.8806262

    Chapter  Google Scholar 

  44. Niu K, Jiao H, Deng N, Gao Z (2016) A real-time fraud detection algorithm based on intelligent scoring for the telecom industry. In: IEEE 2016 international conference on networking and network applications (NaNA), Hakodate, pp 303–306. https://doi.org/10.1109/NaNA.2016.11

    Chapter  Google Scholar 

  45. Zhong R, Dong X, Lin R, Zou H (2019) An incremental identification method for fraud phone calls based on broad learning system. In: 2019 IEEE 19th international conference on communication technology (ICCT), Xi’an, China, pp 1306–1310. https://doi.org/10.1109/ICCT46805.2019.8947271

    Chapter  Google Scholar 

  46. Odusami M, Misra S, Abayomi-Alli O, Abayomi-Alli A, Fernandez-Sanz L (2020) A survey and meta-analysis of application-layer distributed denial-of-service attack. Int J Commun Syst., Wiley 33(18):e4603

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Electronic engineering, Federal Polytechnic Nekede, Owerri, IMO State, Nigeria

    E. O. Nonum

  2. Department of Mechatronics Engineering (Computer Systems & Soft Development), Federal University of Technology, Owerri, Nigeria

    K. C. Okafor

  3. Department of Electronic & Computer Engineering, Nnamdi Azikiwe University, Awka, Nigeria

    I. A. Anthony Nosike

  4. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra

Authors
  1. E. O. Nonum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. C. Okafor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. A. Anthony Nosike
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanjay Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. C. Okafor .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nandu, India

    Amit Kumar Tyagi

  3. Dipartimento di Informatica, Universita' degli Studi di Milano, Milano, Italy

    Vincenzo Piuri

  4. Faculty of Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nonum, E.O., Okafor, K.C., Nosike, I.A.A., Misra, S. (2022). AI-JasCon: An Artificial Intelligent Containerization System for Bayesian Fraud Determination in Complex Networks. In: Misra, S., Kumar Tyagi, A., Piuri, V., Garg, L. (eds) Artificial Intelligence for Cloud and Edge Computing. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-80821-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-80821-1_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80820-4

  • Online ISBN: 978-3-030-80821-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation