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Adaptive Cyber Defense Architecture for Cloud Native Enterprise Applications Using Artificial Intelligence Driven Analytics

Dr.G.Vimal Raja

Principal Consultant, Oracle Financial Service Software Ltd, Bengaluru, India

Abstract

Modern enterprise applications built on cloud-native paradigms face severe cybersecurity challenges due to highly dynamic attack surfaces, containerized ephemeral microservices, and fast-moving threat tactics. Traditional perimeter security and static rule-based Intrusion Detection Systems (IDS) cannot keep pace with zero-day exploits, API abuse, and automated AI-driven attack vectors. This paper introduces an Adaptive Cyber Defense Architecture (ACDA) specifically engineered for cloud-native enterprise environments using artificial intelligence-driven analytics. The proposed framework integrates real-time telemetry streaming across multi-cloud clusters, processing network flows, runtime container logs, and identity access events through containerized deep learning engines. By utilizing behavioral autoencoders, graph neural networks, and reinforcement learning agents, ACDA continuously updates its dynamic security baselines and automatically executes targeted response actions. A decentralized federated learning layer enables multi-region threat intelligence sharing while strictly enforcing data privacy and zero-trust policies. Furthermore, the system includes serverless event-driven orchestration to scale security scanning elastically during workload surges without degrading application performance. Comparative evaluations demonstrate that the architecture cuts mean time to detect (MTTD) and mean time to respond (MTTR) dramatically, reduces false-positive alerts, and maintains high runtime throughput across complex enterprise deployments.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 5 No. 6 (2020): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2020.0506001

Pages

8559-8565

Published

December 7, 2020
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr.G.Vimal Raja (%2020). Adaptive Cyber Defense Architecture for Cloud Native Enterprise Applications Using Artificial Intelligence Driven Analytics. International Journal of Emerging Trends in Engineering and Management Research , Vol. 5 No. 6 (2020): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 8559-8565. https://doi.org/10.15662/ijetemr.2020.0506001

References

1. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
2. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
3. Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27.
4. Watham, S. D., & Vimal, V. R. (2013). Design and Implementation of Data Sanitization Technique For Effective Filtering With Enhanced Medical Support System in Cloud Architecture Diagram. International Journal of Emerging Technology and Advanced Engineering, 3(12), 471-473.
5. Rajendran, S. (2023). Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm.
6. Vayyasi, N. K. (2020). Decoding token volatility patterns with generative models deployed on cloud-native Java environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1552-1565.
7. Mahendran, M., Sugumar, R., Anbazhagan, K., & Natarajan, R. (2012). An efficient algorithm for privacy preserving data mining using heuristic approach. International Journal of Advanced Research in Computer and Communication Engineering, 1(9), 737-744.
8. Sojol, J. I., Shihab, M. H., Ahamed, T., Siam, M. A. S., & Siddique, S. (2019, February). Nirob: a next generation green earth technology based innovative system for metropolitan sound pollution management. In 2019 21st international conference on advanced communication technology (ICACT) (pp. 722-727). IEEE.
9. Yamsani, N. (2018). Operationalizing regulatory governance through enterprise master data design: A practical examination of OFAC, KYC, and GDPR controls at Elavon. International Journal of Scientific Research & Engineering Trends, 4(6). https://doi.org/10.5281/zenodo.18196005
10. Umasankar, P., & Saravanan, K. (2016). Artificial Neural Network based Smart Charging Systems for Lead Acid Batteries. Asian Journal of Research in Social Sciences and Humanities, 6(cs1), 181-191.
11. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
12. Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130-134.
13. 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.
14. Mathew, A. R. (2019). Airport cyber security and cyber resilience controls. arXiv preprint arXiv:1908.09894.
15. Juvvadi, R. R. (2018). Continuous accounting: Toward a real-time financial reporting architecture for the modern enterprise. Computer Fraud & Security, 2018(12), 33–41.
16. Anand, L., & Neelanarayanan, V. (2020, October). Enchanced multiclass intrusion detection using supervised learning methods. In AIP conference proceedings (Vol. 2282, No. 1, p. 020044). AIP Publishing LLC.
17. Parasa, M. (2019). Policy-centric AI control architectures for enterprise software platforms: A governance framework for SAP SuccessFactors. International Journal of Core Engineering and Management, 6(5), 48-67.
18. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
19. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
20. 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