Preventing Cyber-Fraud in Nigeria’s Banking System Using Fraudaeck-AI



Abstract Views
697

Downloads
651

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Oct 30, 2022
Published Dec 19, 2022
10.24018/ejece.2022.6.6.480

Abstract viewed = 697 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

Preventing Cyber-Fraud in Nigeria's Banking System Using Fraudaeck-AI (Fraud analysis environment for cyber-fraud check) is an in-depth analysis of artificial intelligence (AI) assisted identity verification and authentication systems to prevent cyber fraud in Nigeria's banking system. The Fraudaeck (fraud analysis environment for cyber-fraud check) is a machine learning model developed to learn the interconnected subsystems of the communication network and the banking applications and how they function seamlessly to provide customers with ease and comfort of banking even on the go. An investigation revealed how this created a vulnerability in the system, allowing malicious software attacks such as the Xenomorph Trojan to gain access to the system, paving the way for cyber fraudsters—"Yahoo-boys"—to gain access. This paper proposes a solution to cyber-fraud during electronic banking transactions by using an artificial neural network model called Fraudaeck, which can be interfaced with both the telecommunication protocols and the banking application to detect network intrusion, customer identity theft, and prevent cyber-fraud to obtain hitch-free banking activities in Nigeria.
Keywords: Artificial Neural Network, Communication, Cyber-Attack, Cybercrime, ISP, Network; Network Intrusion.

Downloads

Download data is not yet available.

References

  1. Wikipedia. Internet service provider. 2022. [Internet]. Retrieved from: https://en.wikipedia.org/w/index.php?title=Internet_service_provider&oldid=1112903141
     Google Scholar
  2. Joshi N. Biometrics is smart, but AI is smarter Here's why. Allerin. 2019. [Internet] Retrieved from: https://www.allerin.com/blog/biometrics-is-smart-but-ai-is-smarter-heres-why
     Google Scholar
  3. Okeshola FB, Adeta AK. The Nature, Causes and Consequences of Cyber Crime in Tertiary Institutions in Zaria-Kaduna State, Nigeria. American International Journal of Contemporary Research. 2013; 3(9): 98-114.
     Google Scholar
  4. Forenbacher I, Husnjak S, Cviti? I, Jovovi? I. Determinants of mobile phone ownership in Nigeria. (2019). https://www.sciencedirect.com/science/article/pii/S0308596118303665
     Google Scholar
  5. Ibrahim U. The Impact of Cybercrime on the Nigerian Economy and Banking System. NDIC-Quarterly, 2019; 34(12).
     Google Scholar
  6. Alkhalil Z, Hewage C, Nawaf L, Khan I. Phishing Attacks: A Recent Comprehensive Study and a New Anatomy. Frontiers in Computer Science, 2021; 3. https://doi.org/10.3389/fcomp.2021.563060.
     Google Scholar
  7. Ogbonnaya M. Cybercrime in Nigeria demands public-private action. ISS Africa. October 19, 2020. [Internet] Retrieved from: https://issafrica.org/iss-today/cybercrime-in-nigeria-demands-public-private-action.
     Google Scholar
  8. Awodele OM, Adelekan TA, Abodunrin J, George BS, Onosoghenerivwe O, Kajero I, Tanitoluwa O, et al. An Overview of Digital Identity and Digital Identification in Nigeria. (n.d.) Retrieved from: https://digitalrightslawyers.org/wp-content/uploads/2021/01/overview-of-digital-identity-and-digital-identification-in-nigeria-group-4-powerpoint.pdf.
     Google Scholar
  9. Olanrewaju OM, Adebiyi FO. The Impact of Mobile Information and Communication Technology on Cybercrime in Nigeria. International Journal of Engineering Research & Technology (IJERT), 2014; 3(8).
     Google Scholar
  10. Omodunbi BA, Odiase PO, Olaniyan OM, Esan AO. Cybercrimes in Nigeria: Analysis, Detection and Prevention. FUOYE Journal of Engineering and Technology, 2016; 1(1): 37-42. https://doi.org/10.46792/fuoyejet.v1i1.16.
     Google Scholar
  11. Turan WM, Bashir MM. GSM Networks: A Review of Security Threats and Mitigation Measures. The Information Manager, 2006; 6(1&2). https://doi.org/10.4314/tim.v6i1.27226.
     Google Scholar
  12. Bakare BI, Ekolama SM. Preventing Man-in-the-Middle (MiTM) Attack of GSM Calls. European Journal of Electrical Engineering and Computer Science 2021; 5(4): 63-68. https://doi.org/10.24018/ejece.2021.5.4.336.
     Google Scholar
  13. Sonal, Upasna. Mobile Phone Cloning. International Journal of Engineering Research & Technology (IJERT), 2015; 3(10): 1-5, NCETEMS-2015 Conference Proceedings.
     Google Scholar
  14. Raza M, Iqbal M, Sharif M, Haider W. A Survey of Password Attacks and Comparative Analysis on Methods for Secure Authentication. World Applied Sciences Journal, 2012; 19(4): 439-444. https://doi.org/10.5829/idosi.wasj.2012.19.04.1837.
     Google Scholar
  15. Li X, Dai H, Zhao Q. (2014). An Analytical Model on Eavesdropping Attacks in Wireless Networks. IEEE International Conference on Communication Systems, https://ieeexplore.ieee.org/document/7024861/.
     Google Scholar
  16. Azeez NA, Irwin B. Cyber Security: Challenges and the Way Forward. GESJ: Computer Science and Telecommunications, 2010; 6(29): 56-69. Rhodes University.
     Google Scholar
  17. World Bank Group. ID4D Country Diagnostic: Nigeria. 2016. [Internet] Retrieved from: https://documents1.worldbank.org/curated/en/136541489666581589/pdf/113567-REPL-Nigeria-ID4D-Diagnostics-Web.pdf.
     Google Scholar
  18. NIMC. Nigerian Biometric Standards Regulation. 2016 [Internet] Retrieved from: https://www.nimc.gov.ng/docs/Draft_Regulation_on_Nigerian_Biometric_Standards_2016.pdf.
     Google Scholar
  19. Ahmed SI, Hoque R, Guha S, Rifat R, Dell N. Privacy, Security, and Surveillance in the Global South: A Study of Biometric Mobile SIM Registration in Bangladesh. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017: 906?918. https://doi.org/10.1145/3025453.3025961.
     Google Scholar
  20. Dey S, Duttay A, Toledoz JI, Ghoshx SK, Llad J. SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification. Computer Vision Center, 2017. https://www.researchgate.net/publication/318316051_SigNet_Convolutional_Siamese_Network_for_Writer_Independent_Offline_Signature_Verification/figures?lo=1.
     Google Scholar
  21. Rathi M. Convolutional Neural Networks. 2018. [Internet] Retrieved from: https://mukulrathi.com/demystifying-deep-learning/convolutional-neural-network-from-scratch/.
     Google Scholar
  22. Jadon S. An Overview of Deep Learning Architectures in Few-Shot Learning Domain. 2020. [Internet] Retrieved from: https://arxiv.org/pdf/2008.06365.
     Google Scholar
  23. Hu L, Zhang J, Xiang Y, Wang W. Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review. IEEE Access, 2020; 8: 90805-90823, https://doi.org/10.1109/ACCESS.2020.2993562.
     Google Scholar


Most read articles by the same author(s)