Self-learning Fraud Intelligence Engine for Blockchain-based Payment Networks Using Multi-modal Machine Learning
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Abstract
The rapid adoption of blockchain-based payment networks has introduced new challenges in securing digital transactions against sophisticated fraud schemes. Traditional fraud detection mechanisms often struggle with the volume, velocity, and heterogeneity of blockchain transaction data. This study proposes a Self-Learning Fraud Intelligence Engine leveraging multi-modal machine learning to detect and adapt to evolving fraudulent behaviors in blockchain payment systems. The engine integrates transaction history, user behavioral patterns, and network-level data to generate a comprehensive fraud risk profile in real time. By employing adaptive learning algorithms, the system continuously refines its detection models, improving accuracy while minimizing false positives. Experimental simulations on representative blockchain payment scenarios demonstrate significant enhancements in fraud detection performance compared to conventional rule-based and static machine learning approaches. The findings underscore the potential of self-learning, multi-modal AI frameworks in fortifying blockchain payment networks, enabling faster, more reliable, and fraud-resilient financial transactions.
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Mandadhi, V. R. (2024). Self-learning Fraud Intelligence Engine for Blockchain-based Payment Networks Using Multi-modal Machine Learning. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 16(03), 137-142. Retrieved from https://smsjournals.com/index.php/SAMRIDDHI/article/view/3434
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References
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Probability Distribution Fitting to Maternal Mortality Rates in
Nigeria. Asian Journal of Mathematical Sciences.
[31] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram.
(2019). Software-Centric Automation Frameworks Integrating
AI and Cybersecurity Principles. International Journal of
Engineering Science & Humanities, 9(1), 30–40. Retrieved from
https://www.ijesh.com/j/article/view/539
[32] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021).
Autonomous Manufacturing Operations Using Intelligent MES
and Cloud-Native Analytics. Journal of Multidisciplinary
Knowledge, 1(1), 45–55. Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
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Machine Learning Models for Improving Decision Making in
Project Portfolio Management. Darpan International Research
Analysis, 9(1), 12–21. https://doi.org/10.36676/dira.v9.i1.188
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Evaluating the Effectiveness of Green Infrastructure in
Midwestern Cities. Well Testing Journal, 31(2), 74-96.
[35] Bodunwa, O. K., & Makinde, J. O. (2020). Application of Critical
Path Method (CPM) and Project Evaluation Review Techniques
(PERT) in Project Planning and Scheduling. J. Math. Stat. Sci, 6,
1-8.
[36] Sanusi, B. O. Risk Management in Civil Engineering Projects
Using Data Analytics.
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Profession Building on COVID-19’s Innovation Momentum for
Digital, Inclusive Education. OECD Publishing.
[38] Hofbauer, S. (2022). Scenarios in urban public transport 2050:
accommodating for passenger’s basic needs in the design of
vehicles (Doctoral dissertation, Wien).
Reinforcement Learning based Approach for Bridge Health
Maintenance. In Proceedings of the 2023 International Conference
of Computational Science and Computational Intelligence.
Proceedings of the 2023 International Conference on
Computational Science and Computational Intelligence.
[2] Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., ... & He, B. (2021). A
survey on federated learning systems: Vision, hype and reality
for data privacy and protection. IEEE Transactions on Knowledge
and Data Engineering, 35(4), 3347-3366.
[3] Murali, P. K., Kaboli, M., & Dahiya, R. (2022). Intelligent in-vehicle
interaction technologies. Advanced Intelligent Systems, 4(2),
2100122.
[4] ABSALOMOVICH, X. O. DIGITAL ECONOMY.
[5] Saltalamacchia, E. Operational eXcellence by Integrating
Learned information into AcTionable Expertise.
[6] Wan, J., Chen, B., & Wang, S. (2023). Smart manufacturing
factory: Artificial-intelligence-driven customized manufacturing.
CRC Press.
[7] Zielonka, A., Woźniak, M., Garg, S., Kaddoum, G., Piran, M. J.,
& Muhammad, G. (2021). Smart homes: How much will they
support us? A research on recent trends and advances. IEEE
Access, 9, 26388-26419.
[8] Beebe, N. H. (2022). A Complete Bibliography of Publications in
the Journal of Network and Computer Applications.
[9] Murali, P. K., Kaboli, M., & Dahiya, R. (2022). Intelligent in-vehicle
interaction technologies. Advanced Intelligent Systems, 4(2),
2100122.
[10] Riswantini, D., Nugraheni, E., Munandar, D., Arisal, A., &
Suwarningsih, W. (2021). A Survey on Big Data Research in
Fighting COVID-19: Contributions and Techniques.
[11] Mohamed, O. A. M. (2023). How generative AI is transforming
supply chain operations and efficiency?.
[12] Rony, M. M. A., Soumik, M. S., & Akter, F. (2023). Applying Artificial
Intelligence to Improve Early Detection and Containment of
Infectious Disease Outbreaks, Supporting National Public
Health Preparedness. Journal of Medical and Health Studies,
4(3), 82-93.
[13] Oyebode, O. A. (2022). Using Deep Learning to Identify Oil Spill
Slicks by Analyzing Remote Sensing Images (Master’s thesis, Texas
A&M University-Kingsville).
[14] Olalekan, M. J. (2021). Determinants of Civilian Participation Rate
in G7 Countries from (1980-2018). Multidisciplinary Innovations
& Research Analysis, 2(4), 25-42.
[15] Asamoah, A. N. (2022). Global Real-Time Surveillance of
Emerging Antimicrobial Resistance Using Multi-Source
Data Analytics. INTERNATIONAL JOURNAL OF APPLIED
PHARMACEUTICAL SCIENCES AND RESEARCH, 7(02), 30-37.
[16] Pullamma, S. K. R. (2022). Event-Driven Microservices for
Real-Time Revenue Recognition in Cloud-Based Enterprise
Applications. SAMRIDDHI: A Journal of Physical Sciences,
Engineering and Technology, 14(04), 176-184.
[17] Oyebode, O. (2022). Neuro-Symbolic Deep Learning Fused
with Blockchain Consensus for Interpretable, Verifiable, and
Decentralized Decision-Making in High-Stakes Socio-Technical
Systems. International Journal of Computer Applications
Technology and Research, 11(12), 668-686.
[18] SANUSI, B. O. (2023). Performance monitoring and adaptive
management of as-built green infrastructure systems. Well
Testing Journal, 32(2), 224-237.
[19] Rony, M. M. A., Soumik, M. S., & SRISTY, M. S. (2023). Mathematical
and AI-Blockchain Integrated Framework for Strengthening
Cybersecurity in National Critical Infrastructure. Journal of
Mathematics and Statistics Studies, 4(2), 92-103.
[20] Siddique, M. T., Hussain, M. K., Soumik, M. S., & SRISTY, M. S.
(2023). Developing Quantum-Enhanced Privacy-Preserving
Artificial Intelligence Frameworks Based on Physical Principles
to Protect Sensitive Government and Healthcare Data from
Foreign Cyber Threats. British Journal of Physics Studies, 1(1),
46-58.
[21] Olalekan, M. J. (2023). Economic and Demographic Drivers of US
Medicare Spending (2010–2023): An Econometric Study Using
CMS and FRED Data. SAMRIDDHI: A Journal of Physical Sciences,
Engineering and Technology, 15(04), 433-440.
[22] Asamoah, A. N. (2023). The Cost of Ignoring Pharmacogenomics:
A US Health Economic Analysis of Preventable Statin and
Antihypertensive Induced Adverse Drug Reactions. SRMS
[23] Asamoah, A. N. (2023). Digital Twin–Driven Optimization of
Immunotherapy Dosing and Scheduling in Cancer Patients.
Well Testing Journal, 32(2), 195-206.
[24] Asamoah, A. N. (2023). Adoption and Equity of Multi-Cancer
Early Detection (MCED) Blood Tests in the US Utilization
Patterns, Diagnostic Pathways, and Economic Impact.
INTERNATIONAL JOURNAL OF APPLIED PHARMACEUTICAL
SCIENCES AND RESEARCH, 8(02), 35-41.
[25] Odunaike, A. (2023). Time-Varying Copula Networks for
Capturing Dynamic Default Correlations in Credit Portfolios.
Multidisciplinary Innovations & Research Analysis, 4(4), 16-37.
[26] Soumik, M. S., Sarkar, M., & Rahman, M. M. (2021). Fraud
Detection and Personalized Recommendations on Synthetic
E-Commerce Data with ML. Research Journal in Business and
Economics, 1(1a), 15-29.
[27] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram,
Varun Teja Bathini. (2020). Secure Automation Frameworks for
Smart Manufacturing Using Blockchain-Assisted Traceability.
International Journal of Research & Technology, 8(2), 47–53.
Retrieved from https://ijrt.org/j/article/view/879
[28] Zielonka, A., Woźniak, M., Garg, S., Kaddoum, G., Piran, M. J.,
& Muhammad, G. (2021). Smart homes: How much will they
support us? A research on recent trends and advances. IEEE
Access, 9, 26388-26419.
[29] Abusohyon, I. A. S. (2022). IoT and Industry 4.0 technologies in
Digital Manufacturing Transformation.
[30] Isqeel Adesegun, O., Akinpeloye, O. J., & Dada, L. A. (2020).
Probability Distribution Fitting to Maternal Mortality Rates in
Nigeria. Asian Journal of Mathematical Sciences.
[31] Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram.
(2019). Software-Centric Automation Frameworks Integrating
AI and Cybersecurity Principles. International Journal of
Engineering Science & Humanities, 9(1), 30–40. Retrieved from
https://www.ijesh.com/j/article/view/539
[32] Nalluri, S. K., Parasaram, V. K. B., & Bathini, V. T. (2021).
Autonomous Manufacturing Operations Using Intelligent MES
and Cloud-Native Analytics. Journal of Multidisciplinary
Knowledge, 1(1), 45–55. Retrieved from
https://jmk.datatablets.com/index.php/j/article/view/127
[33] Venkata Krishna Bharadwaj Parasaram. (2021). Explainable
Machine Learning Models for Improving Decision Making in
Project Portfolio Management. Darpan International Research
Analysis, 9(1), 12–21. https://doi.org/10.36676/dira.v9.i1.188
[34] SANUSI, B. O. (2022). Sustainable Stormwater Management:
Evaluating the Effectiveness of Green Infrastructure in
Midwestern Cities. Well Testing Journal, 31(2), 74-96.
[35] Bodunwa, O. K., & Makinde, J. O. (2020). Application of Critical
Path Method (CPM) and Project Evaluation Review Techniques
(PERT) in Project Planning and Scheduling. J. Math. Stat. Sci, 6,
1-8.
[36] Sanusi, B. O. Risk Management in Civil Engineering Projects
Using Data Analytics.
[37] Andreas, S. (2022). International Summit on the Teaching
Profession Building on COVID-19’s Innovation Momentum for
Digital, Inclusive Education. OECD Publishing.
[38] Hofbauer, S. (2022). Scenarios in urban public transport 2050:
accommodating for passenger’s basic needs in the design of
vehicles (Doctoral dissertation, Wien).