A Predictive Machine Learning and Big Data Analytics Approach for Optimized Cloud Security Governance and Risk Mitigation Systems
Keywords:
Cloud Security Governance, Predictive Machine Learning, Big Data Analytics, Risk MitigationAbstract
Cloud computing environments have expanded rapidly, introducing complex security governance challenges characterized by dynamic workloads, distributed architectures, and heterogeneous data flows. Traditional rule-based security governance frameworks are increasingly insufficient for managing emerging cyber risks, particularly in large-scale cloud ecosystems. This research proposes a predictive machine learning and big data analytics-driven approach for optimizing cloud security governance and enhancing proactive risk mitigation mechanisms. The study integrates distributed data processing, edge intelligence, and cloud-native analytics to develop a scalable governance framework capable of identifying, predicting, and mitigating security threats in real time.
The proposed model synthesizes insights from IoT data pipelines, fog computing architectures, and AI-based edge inference systems to establish a multi-layered security governance architecture. Special emphasis is placed on adaptive learning mechanisms and distributed AI inference inspired by resource-constrained edge clusters (Zhao, 2018). The framework leverages big data analytics for anomaly detection, predictive risk scoring, and automated policy enforcement across cloud environments. Findings indicate that integrating predictive machine learning significantly improves threat detection latency, enhances governance efficiency, and reduces operational risk exposure in cloud infrastructures.
Downloads
References
1. Alencar, Brenno M., Ricardo A. Rios, Cleber Santana, and Cássio Prazeres. "FoT- Stream: A Fog platform fordata stream analytics in IoT." Computer Communications (2020).
2. Aydin, G., Hallac, I. R., & Karakus, B. (2015). Architecture and implementation of a scalable sensor data storageand analysis system using cloud computing and big data technologies. Journal of Sensors, 2015.
3. Bhandari, S., Sharma, S. K., & Wang, X. (2017, December). Latency minimization in wireless IoT using
4. prioritized channel access and data aggregation. In GLOBECOM 2017-2017 IEEE Global CommunicationsConference (pp. 1-6).
5. Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., Zomaya, A., Energy-efficient data replication in cloud computing datacenters, Cluster Comput., 18 (2015), 385–402
6. Chen, J., & Ran, X. (2019). AI With edge Computing: A Review. Proceedings of the IEEE, 107(8), 1655-1674.
7. Fan, Q., Ansari, N., Application Aware Workload Allocation for edge Computing based IoT, IEEE Int. Things J., (2018), 2146–2153.
8. 7. Fotiou, N., Siris, V. A., Mertzianis, A., & Polyzos, G. C. (2018, June). Smart IoT Data Collection. In 2018 IEEE
9. 19th International Symposium on" A World of Wireless, Mobile and Multimedia Networks"(WoWMoM) (pp.588-599)
10. Kos, A., Tomažič, S., Salom, J., Trifunovic, N., Valero, M., & Milutinovic, V. (2015). New benchmarkingmethodology and programming model for big data goverence. International Journal of Distributed SensorNetworks, 11(8), 271752.
11. Moreira, F., Ferreira, M. J., & Cardoso, A. (2017, July). Higher education disruption through IoT and Big Data:A conceptual approach. In International Conference on Learning and Collaboration Technologies (pp. 389-405).Springer, Cham.
12. Mujawar, A., Kumar, S., Krishnan, S. S., & Sawant, A. (2018). IoT: Green Data Center Strategies. InternationalJournal on Future Revolution in Computer Science & Communication Engineering, 4(5), 170-174.
13. Mulani, N., Pawar, A., Mulay, P., & Dani, A. (2015). Variant of cobweb clustering for privacy preservation incloud db querying. Procedia Computer Science, 50, 363- 368.
14. 12. Rathore, M. M. U., Paul, A., Ahmad, A., Chen, B. W., Huang, B., & Ji, W. (2015). Real-time big data analytical
15. architecture for remote sensing application. IEEE journal of selected topics in applied earth observations andremote sensing, 8(10), 4610-4621.
16. Thirumalai, C., Mohan, S., & Srivastava, G. (2020). An efficient public key secure scheme for cloud and IoT security. Computer Communications, 150, 634-643.
17. Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020). HealthFog: Anensemble AI based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fogcomputing environments. Future Generation Computer Systems, 104, 187-200.
18. Vermesan, O., Blystad, L. C., Zafalon, R., Moscatelli, A., Kriegel, K., Mock, R., ... & Perlo, P. (2011). Internet ofEnergy–connecting Energy anywhere anytime. In Advanced microsystems for automotive applications 2011 (pp.33-48). Springer, Berlin, Heidelberg.
19. Wang, W.Y.C., & Wang, Y. (2020). Analytics in the era of big data: the digital transformations and value creation in industrial marketing.
20. Yi, G., Park, J. H., & Choi, S. (2016). Energy-efficient distributed topology control algorithm for low-power IoTcommunication networks. IEEE Access, 4, 9193-9203.
21. Zhao, Z., Barijough, K. M., & Gerstlauer, A. (2018). Deepthings: Distributed adaptive AI inference on resourceconstrained iot edge clusters. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(11), 2348-2359.
22. Zhu, Y., Jiang, Z., Mo, X., Zhang, B., Al-Dhelaan, A., & Al-Dhelaan, F. (2020). A study on the designmethodology of TAC3 fedge computing [J]. Mathematical Biosciences and Engineering, 17(5), 4406-4421.-4421.
