Authors : Timothy Olatunji Ogundola; Kofoworola Idowu

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/3nse86zd

DOI : https://doi.org/10.38124/ijisrt/26Oct1102

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The increasing sophistication of quantum computing presents significant challenges to the long-term security, computational efficiency, and operational resilience of digital banking infrastructures that rely on conventional public-key cryptographic algorithms. Although Rivest–Shamir–Adleman (RSA), Elliptic Curve Diffie–Hellman (ECDH), and Elliptic Curve Digital Signature Algorithm (ECDSA) remain widely established cryptographic mechanisms, their underlying mathematical assumptions are vulnerable to sufficiently powerful quantum computers. Recent standardization of ModuleLattice-Based Key-Encapsulation Mechanism (ML-KEM), Module-Lattice-Based Digital Signature Algorithm (ML-DSA), and Stateless Hash-Based Digital Signature Algorithm (SLH-DSA) creates opportunities for quantum-resistant financial transaction security, although computational overhead, transmission requirements, and deployment scalability remain important engineering considerations.

Keywords : Post-Quantum Cryptography; PQC-Benchmark Algorithm; Digital Banking Transactions; Cryptographic Performance Evaluation; Hybrid Cryptographic Configurations.

References :

1. Abiodun, K., Ogbuonyalu, U. O., Dzamefe, S., Vera, E. N., Oyinlola, A., & Igba, E. (2023). Exploring cross-border digital assets flows and central bank digital currency risks to capital markets financial stability. International Journal of Scientific Research and Modern Technology, 2(11), 32–45. https://doi.org/10.38124/ijsrmt.v2i11.447
2. Ajayi, A. A., Igba, E., Soyele, A. D., & Enyejo, J. O. (2024). Quantum cryptography and blockchain-based social media platforms as a dual approach to securing financial transactions in CBDCs and combating misinformation in U.S. elections. International Journal of Innovative Science and Research Technology, 9(10), 1409–1424. https://doi.org/10.38124/ijisrt/IJISRT24OCT1697
3. Akanni, F., Idowu, K., Odozor, L., & Waheed, B. (2026). Cyber-financial risk analytics: Integrating business intelligence with threat modeling. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 12(2). https://doi.org/10.32628/CSEIT26121371
4. Alao, M. A., Ayodele, T. R., Ogunjuyigbe, A. S. O., & Popoola, O. M. (2020). Multi-criteria decision based waste to energy technology selection using entropy-weighted TOPSIS technique: The case study of Lagos, Nigeria. Energy, 201, Article 117675. https://doi.org/10.1016/j.energy.2020.117675
5. Atalor, S. I., Ijiga, O. M., & Enyejo, J. O. (2023). Harnessing quantum molecular simulation for accelerated cancer drug screening. International Journal of Scientific Research and Modern Technology, 2(1), 1–18. https://doi.org/10.38124/ijsrmt.v2i1.502
6. Ayoola, V. B., James, U. U., Idoko, I. P., Ijiga, O. M., & Olola, T. M. (2024). Effectiveness of social engineering awareness training in mitigating spear phishing risks in financial institutions from a cybersecurity perspective. Global Journal of Engineering and Technology Advances, 20(3), 94–117. https://doi.org/10.30574/gjeta.2024.20.3.0164
7. Chen, P. (2021). Effects of the entropy weight on TOPSIS. Expert Systems with Applications, 168, Article 114186. https://doi.org/10.1016/j.eswa.2020.114186
8. Doe Gadekah, S, B. & Enyejo, J. O. (2026). The Effects of Generative AI Advertising Disclosure on Brand Authenticity Perceptions Purchase Intention and Consumer Skepticism Among Generation Z Consumers International Journal of Scientific Research in Humanities and Social Sciences Vol, 3 Iss 2, Pg. 137-169 doi : https://doi.org/10.32628/IJSRHSS253293
9. Doe Gadekah, S, B. & Enyejo, J. O. (2025). Personalization Versus Privacy in Digital Marketing Examining the Interactive Effects of Data Transparency Perceived Intrusiveness and Consumer Trust on Purchase Decisions International Journal of Management and Organizational Research Vol. 4, Iss. 6 Page No: 182-199 DOI: https://doi.org/10.54660/IJMOR.2025.4.6.182-199
10. Doe Gadekah, S, B. & Enyejo, J. O. (2025). The Influence of Brand Community Participation and User-Generated Content on Customer-Based Brand Equity Repurchase Intention and Electronic Word-of-Mouth in Direct-to-Consumer Markets International Journal of Management and Organizational Research Vol, 4, Iss. 3 Page No: 183-203 DOI: https://doi.org/10.54660/IJMOR.2025.4.3.183-203
11. Ducas, L., Kiltz, E., Lepoint, T., Lyubashevsky, V., Schwabe, P., Seiler, G., & Stehlé, D. (2018). CRYSTALS-Dilithium: A lattice-based digital signature scheme. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2018(1), 238–268. https://doi.org/10.46586/tches.v2018.i1.238-268
12. Folorunso, T. N., Idowu, K., & Balogun, A. (2025). AI-driven financial risk forecasting: A multi-model ensemble approach for enterprise decision intelligence. Journal of Computational Analysis and Applications, 34(3), 147–173. https://eudoxuspress.com/index.php/pub/article/view/5080
13. Frank, U. P. (2026). Artificial intelligence for infection surveillance: Improving early detection of healthcare-associated infections. USR Journal of Multidisciplinary Research and Studies. [Publication details and DOI not independently confirmed.]
14. Frank, U. P. (2026). Healthcare quality metrics: From data collection to performance improvement; A narrative review of measurement frameworks and improvement methods in United States health care. UKR Journal of Medicine and Medical Research. https://doi.org/10.5281/zenodo.22105362
15. Frank, U. P. (2026). Optimizing medical inventory management through predictive analytics: Implications for healthcare quality, cost reduction, and patient safety; A narrative review. UKR Journal of Education and Literature, 2(1), 77–92. https://doi.org/10.5281/zenodo.22105843
16. Gidney, C., & Ekerå, M. (2021). How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits. Quantum, 5, Article 433. https://doi.org/10.22331/q-2021-04-15-433
17. Gisin, N., Ribordy, G., Tittel, W., & Zbinden, H. (2002). Quantum cryptography. Reviews of Modern Physics, 74(1), 145–195. https://doi.org/10.1103/RevModPhys.74.145
18. Grover, C., Mendelsohn, A., Ling, C., & Vehkalahti, R. (2022). Non-commutative ring learning with errors from cyclic algebras. Journal of Cryptology, 35, Article 22. https://doi.org/10.1007/s00145-022-09430-6
19. Idika, C. N. (2023). Quantum resistant cryptographic protocols for securing autonomous vehicle to vehicle (V2V) communication networks. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(1), 338–360. https://doi.org/10.32628/CSEIT2391547
20. Idoko, I. P., Ijiga, O. M., Enyejo, L. A., Ugbane, S. I., Akoh, O., & Odeyemi, M. O. (2024). Exploring the potential of Elon Musk's proposed quantum AI: A comprehensive analysis and implications. Global Journal of Engineering and Technology Advances, 18(3), 48–65. https://doi.org/10.30574/gjeta.2024.18.3.0037
21. Idowu, K., Cherotich, V., Aniebonam, E. E., Odozor, L., & During, A. (2025). Integrating AI and machine learning into cyber risk management for critical utility systems. International Journal of Artificial Intelligence Engineering and Transformation, 6(2), 32–48. https://doi.org/10.54660/IJAIET.2025.6.2.32-48
22. Idowu, K., Leslie, A., Twayana, S., Nitzan, Y., & Waheed, B. O. (2026). Real-time detection of DDoS and phishing attacks for enhanced banking security. International Journal of Scientific Research in Science, Engineering and Technology, 13(1), 572–593. https://doi.org/10.32628/IJSRSET2613313
23. Igba, E., Olarinoye, H. S., Ezeh, N. V., Sehemba, D. B., Oluhaiyero, Y. S., & Okika, N. (2025). Synthetic data generation using generative AI to combat identity fraud and enhance global financial cybersecurity frameworks. International Journal of Scientific Research and Modern Technology, 4(2), 1–19. https://doi.org/10.5281/zenodo.14928919
24. Ijiga, O. M., Idoko, I. P., Ebiega, G. I., Olajide, F. I., Olatunde, T. I., & Ukaegbu, C. (2024). Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention. Open Access Research Journal of Science and Technology, 11(1). https://doi.org/10.53022/oarjst.2024.11.1.0060
25. Koblitz, N. (1987). Elliptic curve cryptosystems. Mathematics of Computation, 48(177), 203–209. https://doi.org/10.1090/S0025-5718-1987-0866109-5
26. Liu, Z., Pöppelmann, T., Oder, T., Seo, H., Roy, S. S., Güneysu, T., Großschädl, J., Kim, H., & Verbauwhede, I. (2017). High-performance ideal lattice-based cryptography on 8-bit AVR microcontrollers. ACM Transactions on Embedded Computing Systems, 16(4), Article 117. https://doi.org/10.1145/3092951
27. Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26, 369–395. https://doi.org/10.1007/s00158-003-0368-6
28. Montenegro, J. A., Rios, R., & Bonilla, J. (2026). Comparative analysis of post-quantum handshake performance in QUIC and TLS protocols. Computer Networks, 275, Article 111957. https://doi.org/10.1016/j.comnet.2025.111957
29. Montenegro, J. A., Rios, R., & Lopez-Cerezo, J. (2026). A performance evaluation framework for post-quantum TLS. Future Generation Computer Systems, 175, Article 108062. https://doi.org/10.1016/j.future.2025.108062
30. National Institute of Standards and Technology. (2024). Module-lattice-based key-encapsulation mechanism standard (FIPS 203). https://doi.org/10.6028/NIST.FIPS.203
31. Nejatollahi, H., Dutt, N., Ray, S., Regazzoni, F., Banerjee, I., & Cammarota, R. (2019). Post-quantum lattice-based cryptography implementations: A survey. ACM Computing Surveys, 51(6), Article 129. https://doi.org/10.1145/3292548
32. Odozor, L., Berko, S. N., Obu, A. U., & Idowu, K. (2025). Building a resilient U.S. cybersecurity workforce through practical training. International Journal of Scientific and Management Research, 8(8), 30–36. https://doi.org/10.37502/IJSMR.2025.8804
33. Ononiwu, M., Azonuche, T. I., Okoh, O. F., & Enyejo, J. O. (2023). Machine learning approaches for fraud detection and risk assessment in mobile banking applications and fintech solutions. International Journal of Scientific Research in Science, Engineering and Technology, 10(4), 371–390. https://doi.org/10.32628/IJSRSET232531
34. Open Quantum Safe Project. (n.d.). liboqs: Open-source C library for quantum-safe cryptographic algorithms. https://openquantumsafe.org/liboqs/
35. Pirandola, S., Andersen, U. L., Banchi, L., Berta, M., Bunandar, D., Colbeck, R., Englund, D., Gehring, T., Lupo, C., Ottaviani, C., Pereira, J. L., Razavi, M., Shaari, J. S., Tomamichel, M., Usenko, V. C., Vallone, G., Villoresi, P., & Wallden, P. (2020). Advances in quantum cryptography. Advances in Optics and Photonics, 12(4), 1012–1236. https://doi.org/10.1364/AOP.361502
36. Rios, R., & Montenegro, J. A. (2026). Post-quantum cryptography: A multi-layer bottleneck analysis. International Journal of Information Security, 25, Article 89. https://doi.org/10.1007/s10207-026-01242-0
37. Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120–126. https://doi.org/10.1145/359340.359342
38. Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120–126. https://doi.org/10.1145/359340.359342
39. Rubio García, C., Rommel, S., Takarabt, S., Vegas Olmos, J. J., Guilley, S., Nguyen, P., & Tafur Monroy, I. (2024). Quantum-resistant transport layer security. Computer Communications, 213, 345–358. https://doi.org/10.1016/j.comcom.2023.11.010
40. Sahoo, L., Bhurane, A. A., Tolasa, F. T., & Senapati, T. (2026). Integrating K-means clustering with entropy-weighted TOPSIS for large-scale multi-criteria decision making. Scientific Reports. https://doi.org/10.1038/s41598-026-70100-9
41. Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., & Tarantola, S. (2010). Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181(2), 259–270. https://doi.org/10.1016/j.cpc.2009.09.018
42. Sedghighadikolaei, K., & Yavuz, A. A. (2026). A survey of threshold signatures: NIST standards, post-quantum cryptography, exotic techniques, and real-world applications. ACM Computing Surveys, 58(6), Article 143. https://doi.org/10.1145/3772274
43. Shor, P. W. (1997). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5), 1484–1509. https://doi.org/10.1137/S0097539795293172