Blockchain Enabled Zero Trust Security Architecture for Cloud Native Distributed Applications
Abstract
Cloud-native distributed applications have transformed modern computing by enabling scalable, flexible, and resilient digital services through microservices, containers, and distributed infrastructures. However, these environments face increasing cybersecurity threats including unauthorized access, insider attacks, identity compromise, and data breaches. Zero Trust Security Architecture (ZTSA) has emerged as an effective model that continuously verifies users, devices, and services before granting access. The integration of blockchain technology with Zero Trust principles enhances transparency, decentralization, tamper resistance, and secure identity management. This study explores blockchain-enabled Zero Trust security mechanisms for cloud-native distributed applications, examines implementation methodologies, and evaluates their advantages, limitations, and cybersecurity effectiveness.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 7 No. 4 (2022): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
12161-12167 |
Published |
July 5, 2022 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr.S.Jagadeesh Soundappan (%2022). Blockchain Enabled Zero Trust Security Architecture for Cloud Native Distributed Applications. International Journal of Emerging Trends in Engineering and Management Research , Vol. 7 No. 4 (2022): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 12161-12167. https://doi.org/10.15662/ijetemr.2022.0704001 |
References
2. Anderson, R. (2008). Security engineering: A guide to building dependable distributed systems (2nd ed.). Wiley.
3. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
4. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
5. 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.
6. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
7. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
8. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
9. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
10. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
11. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
12. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
13. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
14. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
15. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
16. 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.
17. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
18. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
19. Subramanyam, S. P. (2022). Kubernetes-oriented continuous deployment architecture for .NET microservices. International Journal of Future Innovative Science and Technology (IJFIST), 5(3), 8482–8490. https://doi.org/10.15662/IJFIST.2022.0503002
20. Namdeo, A. (2022). Graph neural networks for real-time supply chain risk. International Journal of Humanities and Information Technology, 4(01-03), 175-192.
21. Yamsani, N. (2021). Governance by design: Secure role delegation and approval structures in enterprise master data systems. International Journal of Science, Engineering and Technology, 9(2). https://doi.org/10.5281/zenodo.18296977
22. Boddupally, H. L. (2020). Model driven engineering of robust data pipelines: Leveraging Entity Framework constructs with SQL Server execution layers. Available at SSRN 6266000.
23. Kanji, R. K. (2020). Federated Learning in Big Data Analytics Privacy and Decentralized Model Training. Journal of Scientific and Engineering Research, 7(3), 343-352.
24. Vayyasi, N. K. (2019). Reimagining financial compliance automation: Using Java microservices and generative AI on AWS Bedrock for regulatory intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 2(3), 1992–1210.
25. Sarabu, V. B. (2018). Architecting Financially Compliant Enterprise Point-of-Sale Systems: A Scalable Data Integrity and Revenue Recognition Framework for Global Retail Platforms. International Journal of Computer Technology and Electronics Communication, 1(2), 329-341.
26. Vankayala, S. C. (2018). Engineering elastic performance testing frameworks for cloud native applications: A scalable design perspective. Journal of Scientific and Engineering Research, 5(8), 301–315. https://doi.org/10.5281/zenodo.17839723
27. Parasa, M. (2022). Addressing the underutilization of exit interview data: A structured AI-assisted framework for actionable workforce insights in SAP SuccessFactors. Global Scientific and Academic Research Journal of Multidisciplinary Studies, 1(6), 42–52. https://gsarpublishers.com/abstract-2326/