Reliable Cloud Microservices Security through AI-Based Event-Driven Messaging and Distributed Transaction Coordination Mechanisms
Main Article Content
Abstract
Cloud microservices architectures enable scalable, flexible, and independently deployable applications through loosely coupled services that communicate using application programming interfaces, message brokers, and event-driven mechanisms. However, distributed execution introduces security and reliability challenges, including unauthorized message access, transaction inconsistencies, service impersonation, event manipulation, and cascading failures. This study examines an integrated framework for improving cloud microservices security through artificial intelligence-based event-driven messaging and distributed transaction coordination mechanisms. The proposed approach combines intelligent event monitoring, anomaly detection, secure message delivery, transaction validation, and coordinated recovery procedures to strengthen the confidentiality, integrity, and availability of distributed applications. Machine learning algorithms analyze message metadata, service interactions, transaction sequences, authentication outcomes, and execution patterns to identify suspicious behavior and potential operational failures. Distributed transaction mechanisms, including the Saga pattern, idempotent processing, event versioning, and compensating transactions, support consistency across independently managed services. The research adopts a quantitative experimental methodology to compare conventional security controls with an AI-enhanced microservices architecture under varying workloads and simulated attack scenarios. Performance is assessed using detection precision, recall, F1-score, transaction completion rate, message-processing latency, recovery time, and resource consumption. The proposed framework aims to integrate adaptive threat detection with reliable transaction coordination while minimizing processing overhead. The study provides a methodological foundation for developing resilient, secure, and intelligent cloud-native applications capable of maintaining operational continuity under dynamic security threats and distributed execution failures.
Article Details
Section
How to Cite
References
1. Hannousse, A., & Yahiouche, S. (2021). Securing microservices and microservice architectures: A systematic mapping study. Computer Science Review, 41, Article 100415. https://doi.org/10.1016/j.cosrev.2021.100415
2. Fahmideh, M., Low, G., Beydoun, G., & Daneshgar, F. (2016). Cloud migration process—A survey, evaluation framework, and open challenges. Journal of Systems and Software, 120, 31–69. https://doi.org/10.1016/j.jss.2016.06.068
3. Neela, S. (2023). Resilient middleware ecosystems: Integrating fault tolerance, recovery-oriented design, and observability for real-time enterprise integration. International Journal of Emerging Research in Engineering and Technology, 4(1), 186–189. https://doi.org/10.63282/3050-922X.IJERET-V4I1P120
4. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26–31.
5. Kalabhavi, V. (2023). Enterprise quote management using AI for product configuration, pricing and approval workflows in SAP CPQ. International Journal of Science, Research and Technology, 6(1), 9231–9242.
6. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
7. Kollu, R. K. (2023). CRM architecture deep dive: Building for scale on Salesforce. International Journal of Engineering & Extended Technologies Research, 5(5), 7309–7312.
8. Chinthamani, R. (2022). Event-driven microservices for scalable retail fulfillment optimization. International Journal of Research and Applied Innovations, 5(2), 6790–6795.
9. Bandhela, R. R., & Kannan, V. (2021). Leveraging generative AI and large language models for secure and efficient healthcare data management. Journal of Informatics Education and Research, 1(3).
10. Tarigoppula, S. (2022). Zero-trust security architecture for hybrid enterprise cloud environments using AWS native services. International Research Journal of Innovative Engineering, 6(6), 11433–11444.
11. Vimal Raja, G. (2022). Leveraging machine learning for real-time short-term snowfall forecasting using multisource atmospheric and terrain data integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336–1339.
12. Pochincharla, S., & Devineni, A. (2023). Automated compliance-driven patch management and security hardening in multi-cloud banking infrastructure. International Journal of Advances in Signal and Image Sciences, 57–62.
13. Kondapalli, K. K., & Gunupudi, C. (2018). A hybrid zero-trust–driven cloud architecture for securing distributed electronic health records in AI-enabled healthcare ecosystems. International Journal of Advanced Research in Engineering and Technology, 9, 116–131.
14. Gunasekaran, R. M. N. (2023). Liquidity Risk Modeling with Machine Learning: Big Data Approaches for Intraday Liquidity Prediction. The Eastasouth Journal of Information System and Computer Science, 1(02), 268–278.
15. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285–2287.
16. Chippagiri, S., & Ravula, P. (2021). Cloud-Native Development: Review of Best Practices and Frameworks for Scalable and Resilient Web Applications. International Journal of New Media Studies: International Peer Reviewed Scholarly Indexed Journal, 8(2), 13–21.
17. Yadavalli, V. R. (2023). Cutover intelligence mesh: A dependency-aware architecture for enterprise cloud ERP migration. International Journal of Future Innovative Science and Technology, 6(4), 11015–11031.
18. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900–2903.
19. Shaik, N. (2023). Zero downtime, zero trust: Automating certificate security at scale. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 8(4), 13973–13988.
20. Puram, S. (2021). Reliable and Scalable Mobile Technology Architecture: A Formal Model, Synchronization Protocol, and Reference Design. The USA Journals TAJET, 3(08), 33–48.
21. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
22. Koganti, H. (2021). Machine learning-driven performance anomaly detection and auto-tuning in distributed Java full-stack systems: A comprehensive review. International Journal of Research Publications in Engineering, Technology and Management, 4(3), 4977–4986.
23. Bitragunta, S. L. V., & Paramasivan, M. (2023). Midterm dynamic simulation for the governance of reserves in systems with elevated renewable energy integration. Journal of Artificial Intelligence, 1(1), 1956–1962.
24. Soundappan, S. J. (2022). Distributed Cloud Architecture for Intelligent Performance Optimization and Proactive Real-Time Threat Detection. International Research Journal of Innovative Engineering, 6(6), 11426–11432.
25. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162–8169.
26. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology (IJSRAT), 5(3), 7777–7783. https://www.ijsrat.com/index.php/ijsrat/issue/view/23
27. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273–287.
28. Selvarajan, K. (2023). Improving data reliability through automated data quality frameworks in distributed systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10630–10640.
29. Sugumar, R. (2021). Generative AI Pipelines for Safety Validation of Autonomous Driving Models. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(5), 5466–5469.
30. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Science, Research and Technology, 4(3), 5731–5744.
31. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
32. Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12179–12190.
33. Padmanabham, S. (2023). Building resilient Banking platforms using Event-Driven Microseconds and cloud Native Architecture. International Journal of Research and Applied Innovations, 6(4), 9284–9290.
34. Sen, A., & Madria, S. (2020). Application design phase risk assessment framework using cloud security domains. Journal of Information Security and Applications, 55, Article 102617. https://doi.org/10.1016/j.jisa.2020.102617