Predictive Analytics and Machine Learning Framework for Secure Enterprise Platforms and Intelligent Infrastructure Management
DOI:
https://doi.org/10.15680/IJCTECE.2024.0704012Keywords:
Predictive analytics, Machine learning, Enterprise platforms, Intelligent infrastructure, Security, Data-driven decision-making, Threat detection, AutomationAbstract
Predictive analytics and machine learning (ML) have emerged as essential technologies for modern enterprise platforms, enabling organizations to optimize operations, enhance security, and manage infrastructure intelligently. This paper proposes a comprehensive framework that integrates predictive analytics and ML algorithms to anticipate operational bottlenecks, detect security threats, and automate infrastructure management in enterprise environments. The framework leverages historical data, real-time monitoring, and advanced statistical models to provide actionable insights, improve decision-making, and reduce downtime. Security considerations are embedded within the ML pipeline to ensure data confidentiality, integrity, and compliance with organizational policies. Additionally, the framework supports scalability, interoperability, and integration with existing enterprise systems, enabling a seamless transition to intelligent infrastructure management. Case studies and simulation results demonstrate the efficacy of the proposed approach in enhancing predictive accuracy, operational efficiency, and proactive threat mitigation. This framework provides a roadmap for organizations seeking to adopt data-driven strategies to manage enterprise platforms securely and efficiently. Future research directions include incorporating adaptive learning, edge computing, and explainable AI to improve model interpretability and responsiveness in dynamic enterprise environments.
References
1. Luo, M., & Zhang, L.-J. (2023). Advances in cloud computing architectures and AI-enabled services. In Cloud computing – CLOUD 2023. Springer.
2. Sanepalli, Uttama Reddy. (2024). GitOps security architecture with zero trust: Identity-driven control planes for cloud-native deployments. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(2), 1198–1209. https://doi.org/10.32628/CSEIT24102255
3. Konda, S. K. (2024). Carbon-native DCIM architectures for AI data centers: Autonomous infrastructure control via smart grid intelligence. World Journal of Advanced Research and Reviews, 21(1), 3008–3318. https://doi.org/10.30574/wjarr.2024.21.1.0095
4. Bhatnagar, G., Rajoria, Y. K., Sakeel, M., Vigenesh, M., Premananthan, G., & Dongre, D. (2023, September). IoT malware detection tool with CNN classification for small devices. In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 6, pp. 2017-2023). IEEE.
5. Sivanantham, E., Vijayakumar, R., Veda, P., Nithya, A., Vinayagam, P. V., & Renukadevi, S. (2024, April). Optimizing Smart Methane Farms: Intelligent Waste Sorting for Maximum Biogas Yield through Naive Bayes and IoT Integration. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1205-1210). IEEE.
6. Rengarajan, A., & Rajagopalan, S. (2021). Chaos Blend LFSR-Duo Approach on FPGA for Medical Image Security. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 3, 3, 155.
7. Ramsugeerthi, A., Neela Madheswari, A., Umamaheswari, A., & Prassana, D. (2020). Location navigation assistance for educational institutions using augmented reality. Journal of Xidian University, 14(4), 1342–1347. https://doi.org/10.37896/jxu14.4/156
8. Ravi Kumar Ireddy, “Real-Time Payment Orchestration and Fraud Governance Framework: Cloud-Native Treasury Optimization with Ensemble Deep Learning Integration”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 3, pp. 1152–1161, Jun. 2024, doi: 10.32628/CSEIT25113583.
9. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
10. Balaji, K. V., & Sugumar, R. (2023, December). Harnessing the Power of Machine Learning for Diabetes Risk Assessment: A Promising Approach. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-6). IEEE.
11. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5342-5351.
12. Potel, R. (2022). AI-Driven Security Graphs for Real-Time Breach Containment in Hybrid Cloud Environments. International Journal of AI, BigData, Computational and Management Studies, 3(4), 123-131.
13. Meka, S. (2022). Engineering Insurance Portals of the Future: Modernizing Core Systems for Performance and Scalability. International Journal of Computer Science and Information Technology Research, 3(1), 180-198.
14. Madathala, H., Barmavat, B., & Thumala, S. (2023). Performance optimization of sap hana using ai-based workload predictions. International Journal of Innovative Research in Science, Engineering and Technology, 12, 15315-15326.
15. Karnam, A. (2024). Next-Gen Observability for SAP: How Azure Monitor Enables Predictive and Autonomous Operations. International Journal of Computer Technology and Electronics Communication, 7(2), 8515–8524. https://doi.org/10.15680/IJCTECE.2024.0702006
16. G. Sarraf, “Autonomous Ransomware Forensics: Advanced ML Techniques for Attack Attribution and Recovery,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 3, pp. 1377–1390, Jul. 2023, doi: 10.48175/IJARSCT-11978W
17. 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.
18. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
19. Devi, C., Musunuru, M. V., & Mohammed, A. S. (2023). Reinforcement-Learning Scheduler for Multi-Tenant Spark Clustersunder Privacy Constraints. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 496-527.
20. Panda, S. S. (2023). Agile Quality in the Cloud Leading Azure RDOS Testing and Release Management. International Journal of Humanities and Information Technology, 5(02), 19-25.
21. Paul, D., Namperumal, G., & Surampudi, Y. (2023). Optimizing llm training for financial services: best practices for model accuracy, risk management, and compliance in ai-powered financial applications. Journal of Artificial Intelligence Research and Applications, 3(2), 550-588.
22. Mangukiya, M. (2023). Blockchain-Enabled Traceability and Compliance in Global Electronics Production Networks. International Journal of Computer Technology and Electronics Communication, 6(6), 7999-8004.
23. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human–Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
24. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
25. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
26. Vootla, A. (2023). Continuous Accessibility Assurance through DevSecOps-Integrated Testing Pipelines. International Journal of Research and Applied Innovations, 6(6), 9975-9984.
27. Kothokatta, L. (2023). AI-Augmented Quality Engineering for MLOps: Intelligent Test Orchestration and Model Reliability on AWS. International Journal of Computer Technology and Electronics Communication, 6(4), 7324-7330.
28. Dave, B. L. (2023). Enhancing Vendor Collaboration via an Online Automated Application Platform. International Journal of Humanities and Information Technology, 5(02), 44-52.
29. 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.
30. Rao, N. S., Shanmugapriya, G., Vinod, S., & Mallick, S. P. (2023, March). Detecting human behavior from a silhouette using convolutional neural networks. In 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) (pp. 943-948). IEEE.
31. Gurumoorthy, T. Neuro Fuzzy Sliding Mode Control Technique for Voltage Tracking In Boost Converter.
32. 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.
33. 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.
34. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
35. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
36. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
37. 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.
38. Dama, H. B. (2023). Designing Highly Available Multi-Cloud Database Architectures for Global Financial Services. International Journal of Research and Applied Innovations, 6(1), 8329-8336.
39. Karvannan, R. (2023). Real‑Time Prescription Management System Intake & Billing System. International Journal of Humanities and Information Technology, 5(02), 34-43.
40. Neustein, A., Mahalle, P. N., Joshi, P., & Shinde, G. R. (Eds.). (2023). AI, IoT, big data and cloud computing for Industry 4.0. Springer. https://doi.org/10.1007/978-3-031-29713-7
