Preventing Cyber-Fraud in Nigeria’s Banking System Using Fraudaeck-AI
##plugins.themes.bootstrap3.article.sidebar##
PDF
Submitted Oct 30, 2022
Published Dec 19, 2022
Published Dec 19, 2022
DOI
Abstract viewed = 71 times
Issue
Section
Articles
##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.
References
Wikipedia. Internet service provider. 2022. [Internet]. Retrieved from: https://en.wikipedia.org/w/index.php?title=Internet_service_provider&oldid=1112903141
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
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.
Forenbacher I, Husnjak S, Cviti? I, Jovovi? I. Determinants of mobile phone ownership in Nigeria. (2019). https://www.sciencedirect.com/science/article/pii/S0308596118303665
Ibrahim U. The Impact of Cybercrime on the Nigerian Economy and Banking System. NDIC-Quarterly, 2019; 34(12).
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.
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.
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.
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).
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.
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.
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.
Sonal, Upasna. Mobile Phone Cloning. International Journal of Engineering Research & Technology (IJERT), 2015; 3(10): 1-5, NCETEMS-2015 Conference Proceedings.
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.
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/.
Azeez NA, Irwin B. Cyber Security: Challenges and the Way Forward. GESJ: Computer Science and Telecommunications, 2010; 6(29): 56-69. Rhodes University.
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.
NIMC. Nigerian Biometric Standards Regulation. 2016 [Internet] Retrieved from: https://www.nimc.gov.ng/docs/Draft_Regulation_on_Nigerian_Biometric_Standards_2016.pdf.
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.
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.
Rathi M. Convolutional Neural Networks. 2018. [Internet] Retrieved from: https://mukulrathi.com/demystifying-deep-learning/convolutional-neural-network-from-scratch/.
Jadon S. An Overview of Deep Learning Architectures in Few-Shot Learning Domain. 2020. [Internet] Retrieved from: https://arxiv.org/pdf/2008.06365.
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.
Downloads
Download data is not yet available.
##plugins.themes.bootstrap3.article.details##
How to Cite
[1]
Ekolama, S.M., Orike, S. and Elechi, P. 2022. Preventing Cyber-Fraud in Nigeria’s Banking System Using Fraudaeck-AI. European Journal of Electrical Engineering and Computer Science. 6, 6 (Dec. 2022), 55–63. DOI:https://doi.org/10.24018/ejece.2022.6.6.480.
This work is licensed under a Creative Commons Attribution 4.0 International License.
