Articles Open Access
Federated Learning Models for Privacy-Preserving Security Analytics across Distributed Enterprise Cloud Environments
School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
Abstract
Federated learning has emerged as a distributed machine-learning paradigm for organizations seeking collaborative cybersecurity intelligence without transferring sensitive raw data to a centralized repository. Modern enterprises generate security information across public clouds, private clouds, hybrid infrastructures, endpoints, networks, applications, containers, identities, and databases. Although these datasets are valuable for detecting cyber threats, their centralization can introduce privacy, regulatory, data-sovereignty, and security concerns. Federated learning enables multiple enterprise environments to train a shared machine-learning model locally and exchange model parameters or updates rather than the underlying security records. This paper examines federated learning models for privacy-preserving security analytics across distributed enterprise cloud environments. The proposed framework integrates local security analytics, federated model training, secure aggregation, differential privacy, encrypted communication, participant authentication, and robust aggregation. It considers important challenges including heterogeneous and non-identically distributed security data, communication overhead, model convergence, privacy leakage, malicious participants, model poisoning, and operational scalability. A controlled experimental methodology is proposed in which multiple enterprise environments independently process security telemetry and participate in coordinated federated training. The framework is evaluated using detection accuracy, precision, recall, F1-score, false-positive rate, convergence time, communication overhead, computational cost, privacy protection, and resilience against adversarial behavior. The study establishes a methodological foundation for collaborative security analytics that seeks to balance cybersecurity effectiveness with privacy, data sovereignty, and distributed enterprise governance
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 7 |
| Number | 6 |
| Pages | 13629-13637 |
| Published | 2023-12-12 |
| Issue | Vol. 7 No. 6 (2023): International Research Journal of Innovative Engineering (IRJIE) |
References
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30. Bitragunta, S. L. V. (2022, November 20). High level modeling of high-voltage gallium nitride (GaN) power devices for sophisticated power electronics applications. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 2011–2015.
31. Bhattacharjee, B., Khan, M. A. N., Enayet, M. A., Gazi, M. S., Tasnim, M., Jakir, T., ... & Himeluzzaman, M. (2021). Adversarial Machine Learning and Cognitive AI for Autonomous Defense against Advanced Cyber Threats. International Journal of Computer Technology and Electronics Communication, 4(6), 4327-4337.
32. Bellundagi, M. (2023). Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039.
33. Belal, M. M., & Sundaram, D. M. (2022). Comprehensive review on intelligent security defences in cloud: Taxonomy, security issues, ML/DL techniques, challenges and future trends. Journal of King Saud University - Computer and Information Sciences, 34(10), 9102–9131. https://doi.org/10.1016/j.jksuci.2022.08.035
2. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology, 5(3), 7777-7783.
3. Ramasamy, M. (2022). A reference architecture for AI-driven network assurance in large-scale enterprise networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4336–4346.
4. Reddy, K. V. (2022). Power-aware verification closure using AI-enhanced UVM and dynamic voltage and frequency scaling models. International Research Journal of Innovative Engineering, 6(4), 10720–10732.
5. Mishra, D., Behera, D., Challa, A., & Vemu, C. (2021). PDCloudEX software defined network. In Integrated emerging methods of artificial intelligence & cloud computing (pp. 66–70). Springer International Publishing.
6. Bhattacharjee, B., Khan, M. A. N., Enayet, M. A., Gazi, M. S., Tasnim, M., Jakir, T., ... & Himeluzzaman, M. (2021). Adversarial Machine Learning and Cognitive AI for Autonomous Defense against Advanced Cyber Threats. International Journal of Computer Technology and Electronics Communication, 4(6), 4327-4337.
7. Badam, L. R. (2023). AI-driven real-time cyberattack detection for critical financial infrastructure. International Journal of Science, Research and Technology (IJSRAT), 6(4), 10374–10383.
8. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143-156.
9. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep Learning–based Dynamic User Alignment in Social Networks. ACM Journal of Data and Information Quality, 15(3), 1-26.
10. Selvarajan, K. (2022). Cloud-native architectures for high-throughput distributed data processing. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(5), 12538–12548.
11. Kavuru, R. R., & Balaji, R. (2023). Extending the service mesh into a compliance enforcement fabric: A policy-aware architecture for regulated financial platforms. International Journal of Emerging Trends in Engineering and Management Research, 8(3), 13612–13618.
12. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
13. Shalk, N. (2022). Beyond vendor lock-in: Building intelligent multi-cloud Kubernetes orchestration. International Research Journal of Innovative Engineering, 6(2), 9985–9996.
14. Koganti, H. (2021). Machine learning-driven performance anomaly detection and auto-tuning in distributed Java full-stack systems: A comprehensive review. International Journal of Research Publications in Engineering, Technology and Management, 4(3), 4977–4986.
15. Neela, S. (2022). Semantic Interoperability in Real-Time Enterprise Integration Using Middleware Abstractions. American International Journal of Computer Science and Technology, 4(6), 56-66.
16. Yadavalli, V. R. (2022). Continuous trust scoring for cross-layer access governance in S/4HANA, Fiori, and SAP BTP. International Journal of Engineering & Extended Technologies Research, 4(1), 4347–4357.
17. 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.
18. Devineni, A., Pochincharla, S., & Muppalla, L. K. (2022). A telemetry-fusion framework for proactive incident detection in multi-tenant financial cloud platforms. International Journal of Advances in Signal and Image Sciences, 13–18.
19. Tarigoppula, S. (2022). Zero-trust security architecture for hybrid enterprise cloud environments using AWS native services. International Research Journal of Innovative Engineering, 6(6), 11433–11444.
20. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
21. 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.
22. Basavala, S. R. (2022). Security-by-design: Threat modeling and requirements for enterprise applications. International Journal of Science, Research and Technology, 5(1), 7168–7178.
23. Roy, S., & Sen, A. (2020, September). Identification of the K-most Vulnerable Entities in a Smart Grid System. In 2020 3rd International Conference on Advanced Communication Technologies and Networking (CommNet) (pp. 1-6). IEEE.
24. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
25. RamMohan Reddy Kundavaram. (2019). Utilizing AI and machine learning in cloud systems for advanced automation. Economic Sciences, 15(1), 28–38. https://doi.org/10.69889/4tx3gc35
26. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
27. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
28. Sugumar, R. (2021). Generative AI Pipelines for Safety Validation of Autonomous Driving Models. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(5), 5466-5469.
29. 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.
30. Bitragunta, S. L. V. (2022, November 20). High level modeling of high-voltage gallium nitride (GaN) power devices for sophisticated power electronics applications. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 2011–2015.
31. Bhattacharjee, B., Khan, M. A. N., Enayet, M. A., Gazi, M. S., Tasnim, M., Jakir, T., ... & Himeluzzaman, M. (2021). Adversarial Machine Learning and Cognitive AI for Autonomous Defense against Advanced Cyber Threats. International Journal of Computer Technology and Electronics Communication, 4(6), 4327-4337.
32. Bellundagi, M. (2023). Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039.
33. Belal, M. M., & Sundaram, D. M. (2022). Comprehensive review on intelligent security defences in cloud: Taxonomy, security issues, ML/DL techniques, challenges and future trends. Journal of King Saud University - Computer and Information Sciences, 34(10), 9102–9131. https://doi.org/10.1016/j.jksuci.2022.08.035
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