Next-Gen Intelligent AI Cloud for Fraud Detection and Cybersecurity Defense: Time-Optimized ML Architectures with Deep RiskPredict Intelligence
Keywords:
Next-Generation Cloud AI, Fraud Detection, Cybersecurity Defense, Time-Optimized Machine Learning, Deep RiskPredict Intelligence, Real-Time Threat Analytics, Cloud-Native Security Architecture, Adaptive Risk Scoring, Streaming Data Integration, Deep Neural Networks (DNN), Multivariate Behavioral Analysis, Intelligent Decision SystemsAbstract
The rapid rise of digital transactions, multi-tenant cloud platforms, and evolving cyber threats has intensified the need for intelligent, scalable, and real-time security frameworks. This paper introduces a Next-Generation Intelligent AI Cloud Framework that unifies fraud detection and cybersecurity defense through time-optimized machine learning architectures and Deep RiskPredict Intelligence. The framework leverages cloud-native data pipelines to integrate heterogeneous streaming data—including transactional logs, behavioral signals, network telemetry, and contextual metadata—enabling continuous monitoring and rapid incident response in large-scale environments.
Central to the design is a suite of time-optimized ML models and deep learning–based RiskPredict engines that balance computational efficiency with predictive accuracy, making the framework suitable for latency-sensitive and resource-constrained operational settings. The RiskPredict module incorporates deep neural networks, multivariate feature interactions, and adaptive risk scoring mechanisms to identify emerging fraud patterns and cybersecurity threats in real time.
Empirical evaluation demonstrates significant reductions in detection latency, improvements in fraud and threat classification accuracy, and enhanced system efficiency compared to conventional ML and rule-based approaches. The proposed framework establishes an advanced, cloud-ready security model capable of evolving with complex threat landscapes while supporting proactive defense and financial risk intelligence. It contributes a robust foundation for next-generation AI-driven security systems deployed across diverse and high-demand cloud ecosystems.
References
1. Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. _Proceedings of the 41st International Conference on Software Engineering_.
2. Balaji, K. V., Sugumar, R., Mahendran, R., & Subramanian, P. (2025). Weather forecasting model using attentive residual gated recurrent unit for urban flood prediction. GLOBAL NEST JOURNAL, 27(5).
3. Althati, C., Tomar, M., & Malaiyappan, J. N. A. (2024). Scalable machine learning solutions for heterogeneous data in distributed data platform. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 4(1), 299-309.
4. Vasugi, T. (2023). AI-empowered neural security framework for protected financial transactions in distributed cloud banking ecosystems. International Journal of Advanced Research in Computer Science & Technology, 6(2), 7941–7950. https://doi.org/0.15662/IJARCST.2023.0602004
5. Hardial Singh, “Strengthening Endpoint Security to Reduce Attack Vectors in Distributed Work Environments”, International Journal of Management, Technology And Engineering, Volume XIV, Issue VII, JULY 2024.
6. Uddandarao, D. P. Improving Employment Survey Estimates in Data-ScarceRegions Using Dynamic Bayesian Hierarchical Models: Addressing Measurement Challenges in Developing Countries. Panamerican Mathematical Journal, 34(4), 2024. https://doi.org/10.52783/pmj.v34.i4.5584
7. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
8. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. _Foundations and Trends in Theoretical Computer Science, 9_(3–4), 211–407.
9. Fielding, R. T., & Taylor, R. N. (2000). Architectural styles and the design of network-based software architectures. _PhD Thesis, University of California, Irvine_.
10. Lipton, Z. C. (2018). The mythos of model interpretability. _Communications of the ACM, 61_(10), 36–43.
11. Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., & Brockman, G. (2021). Evaluating large language models trained on code. _arXiv preprint arXiv:2107.03374_.
12. Humble, J., Molesky, J., & O'Reilly, J. (2010). Release It!: Design and Deploy Production-Ready Software. _Pragmatic Bookshelf_.
13. Muthusamy, M. (2024). Cloud-Native AI metrics model for real-time banking project monitoring with integrated safety and SAP quality assurance. International Journal of Research and Applied Innovations (IJRAI), 7(1), 10135–10144. https://doi.org/10.15662/IJRAI.2024.0701005
14. Kandula, N. (2023). Evaluating Social Media Platforms A Comprehensive Analysis of Their Influence on Travel Decision-Making. J Comp Sci Appl Inform Technol, 8(2), 1-9.
15. Muthusamy, P., Thangavelu, K., & Bairi, A. R. (2023). AI-Powered Fraud Detection in Financial Services: A Scalable Cloud-Based Approach. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 146-181.
16. Kumar, S. N. P. (2022). Improving Fraud Detection in Credit Card Transactions Using Autoencoders and Deep Neural Networks (Doctoral dissertation, The George Washington University).
17. Konda, S. K. (2022). STRATEGIC EXECUTION OF SYSTEM-WIDE BMS UPGRADES IN PEDIATRIC HEALTHCARE ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7123-7129.
18. Rahman, M. R., Tohfa, N. A., Arif, M. H., Zareen, S., Alim, M. A., Hossen, M. S., ... & Bhuiyan, T. (2025). Enhancing android mobile security through machine learning-based malware detection using behavioral system features. https://www.researchgate.net/profile/Nasrin-Tohfa/publication/397379591_Enhancing_android_mobile_security_through_machine_learning-based_malware_detection_using_behavioral_system_features/links/6912b141c900be105cc0b8b6/Enhancing-android-mobile-security-through-machine-learning-based-malware-detection-using-behavioral-system-features.pdf
19. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
20. Kumar, R. K. (2024). Real-time GenAI neural LDDR optimization on secure Apache–SAP HANA cloud for clinical and risk intelligence. IJEETR, 8737–8743. https://doi.org/10.15662/IJEETR.2024.0605006
21. Perumalsamy, J., & Pichaimani, T. (2024). InsurTechPredict: AI-driven Predictive Analytics for Claims Fraud Detection in Insurance. American Journal of Data Science and Artificial Intelligence Innovations, 4, 127-163.
22. Arora, Anuj. "Detecting and Mitigating Advanced Persistent Threats in Cybersecurity Systems." Science, Technology and Development, vol. XIV, no. III, Mar. 2025, pp. 103–117.
23. Nagarajan, G. (2024). Cloud-Integrated AI Models for Enhanced Financial Compliance and Audit Automation in SAP with Secure Firewall Protection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(1), 9692-9699.
24. Binu, C. T., Kumar, S. S., Rubini, P., & Sudhakar, K. (2024). Enhancing Cloud Security through Machine Learning-Based Threat Prevention and Monitoring: The Development and Evaluation of the PBPM Framework. https://www.researchgate.net/profile/Binu-C-T/publication/383037713_Enhancing_Cloud_Security_through_Machine_Learning-Based_Threat_Prevention_and_Monitoring_The_Development_and_Evaluation_of_the_PBPM_Framework/links/66b99cfb299c327096c1774a/Enhancing-Cloud-Security-through-Machine-Learning-Based-Threat-Prevention-and-Monitoring-The-Development-and-Evaluation-of-the-PBPM-Framework.pdf
25. Chiranjeevi, Y., Sugumar, R., & Tahir, S. (2024, November). Effective Classification of Ocular Disease Using Resnet-50 in Comparison with Squeezenet. In 2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-6). IEEE.
26. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
27. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2024). Artificial Neural Network in Fibre-Reinforced Polymer Composites using ARAS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 9801-9806.
28. McDaniel, P., & Priebe, C. (2011). Security and privacy in healthcare information systems. _Journal of Healthcare Information Management, 25_(3), 32–45.
