AI-Driven Automation and Learning Techniques for Enhanced Cloud and Network Security in Flash Storage and Healthcare ERP Systems

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Published: 2021-12-10
DOI: https://doi.org/10.15662/IJRPETM.2021.0406007
Keywords:
AI-driven automation, Reinforcement learning, Multivariate classification, Cloud security, Network security, Flash storage security, Healthcare ERP cybersecurity, Machine learning, Anomaly detection, Intelligent threat mitigation

Main Article Content

João Felipe Ribeiro Machado Alves
Cloud Architect, Amapá, Brazil

Abstract

The rapid expansion of cloud infrastructures and distributed network architectures has intensified the demand for intelligent, scalable security solutions capable of mitigating increasingly sophisticated cyber threats. This paper examines the integration of AI-driven automation, reinforcement learning, and multivariate classification techniques to enhance threat detection, incident response, and adaptive defense mechanisms within cloud and network environments. Particular emphasis is placed on the challenges and opportunities associated with securing flash-based storage systems and healthcare Enterprise Resource Planning (ERP) platforms, both of which process high-volume, high-sensitivity data. The study explores how machine learning models can identify anomalous patterns across complex multivariate datasets, while reinforcement learning agents optimize continuous defensive decision-making in dynamic threat landscapes. Additionally, the role of AI-driven automation in predicting storage-level vulnerabilities, improving data integrity, and strengthening healthcare ERP security workflows is analyzed. The findings suggest that the convergence of AI methodologies and advanced storage architectures provides a robust foundation for proactive, adaptive, and resilient cybersecurity strategies, particularly in sectors requiring stringent regulatory compliance and real-time data processing.

Article Details

Issue

Vol. 4 No. 6 (2021): International Journal of Research Publications in Engineering, Technology and Management

Section

Articles

How to Cite

AI-Driven Automation and Learning Techniques for Enhanced Cloud and Network Security in Flash Storage and Healthcare ERP Systems. (2021). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(6), 5909-5916. https://doi.org/10.15662/IJRPETM.2021.0406007

References

1. Al-Jarrah, O. Y., Yoo, P. D., Muhaidat, S., Karagiannidis, G. K., & Taha, K. (2016). Data randomization and clustering for intrusion detection in big data networks. IEEE Access, 4, 1722–1735. https://doi.org/10.1109/ACCESS.2016.2543838

2. Sabin Begum, R., & Sugumar, R. (2019). Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud. Cluster Computing, 22(Suppl 4), 9581-9588.

3. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004

4. 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.

5. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.

6. Navandar, P. (2021). Fortifying cybersecurity in Healthcare ERP systems: unveiling challenges, proposing solutions, and envisioning future perspectives. Int J Sci Res, 10(5), 1322-1325.

7. Das, D., Vijayaboopathy, V., & Rao, S. B. S. (2018). Causal Trace Miner: Root-Cause Analysis via Temporal Contrastive Learning. American Journal of Cognitive Computing and AI Systems, 2, 134-167.

8. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.

9. Hardial Singh, “ENHANCING CLOUD SECURITY POSTURE WITH AI-DRIVEN THREAT DETECTION AND RESPONSE MECHANISMS”, INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), VOLUME-6, ISSUE-2, 2019.

10. Li, W., & Luo, R. (2017). A multivariate intrusion detection method based on deep belief networks. Journal of Information Security and Applications, 35, 165–170. https://doi.org/10.1016/j.jisa.2017.06.005Mnih, V. et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533. https://doi.org/10.1038/nature14236Shin, D., & Lee, S. (2019). Flash storage technologies for cloud computing: Security challenges and opportunities. ACM Computing Surveys, 51(6), 1–37. https://doi.org/10.1145/3277609

11. Sundararaman, S., Balakrishnan, M., & Prabhakaran, V. (2011). FlashStore: High throughput persistent key-value store. Proceedings of the VLDB Endowment, 3(1–2), 111–122.

12. Konidena, B. K., Bairi, A. R., & Pichaimani, T. (2021). Reinforcement Learning-Driven Adaptive Test Case Generation in Agile Development. American Journal of Data Science and Artificial Intelligence Innovations, 1, 241-273.

13. Thangavelu, K., Sethuraman, S., & Hasenkhan, F. (2021). AI-Driven Network Security in Financial Markets: Ensuring 100% Uptime for Stock Exchange Transactions. American Journal of Autonomous Systems and Robotics Engineering, 1, 100-130.

14. Anuj Arora, “Analyzing Best Practices and Strategies for Encrypting Data at Rest (Stored) and Data in Transit (Transmitted) in Cloud Environments”, “INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING”, VOL. 6 ISSUE 4 ( OCTOBER- DECEMBER 2018).

15. Kapadia, V., Jensen, J., McBride, G., Sundaramoothy, J., Deshmukh, R., Sacheti, P., & Althati, C. (2015). U.S. Patent No. 8,965,820. Washington, DC: U.S. Patent and Trademark Office.

16. Peddamukkula, P. K. (2021). Ethical considerations in AI and automation integration within the life insurance industry. International Journal of Innovative Research in Computer and Communication Engineering, 9(9), 9701–9709. https://doi.org/10.15680/IJIRCCE.2021.0909001

17. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.

18. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.

19. 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.

20. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.

21. Zhang, Y., Qian, Y., Wu, Y., & Yu, R. (2018). Machine learning-based network security assessment in cloud environments. IEEE Transactions on Cloud Computing, 6(3), 719–731. https://doi.org/10.1109/TCC.2015.2511754