Articles Open Access
Privacy-Preserving AI-Based Intrusion Detection Using Federated Learning and Adaptive Analytics in Enterprise Cloud Networks
Department of Computing and Informatics, Botswana International University of Science and Technology, Botswana, South Africa
Abstract
The increasing adoption of enterprise cloud networks has created complex cybersecurity challenges due to distributed infrastructure, interconnected applications, dynamic workloads, and continuously evolving cyber threats. Traditional intrusion detection systems often depend on centralized data collection, which introduces privacy risks, communication overhead, and limitations in protecting sensitive enterprise information. This research proposes a privacy-preserving artificial intelligence-based intrusion detection framework that integrates federated learning and adaptive analytics for securing enterprise cloud networks. The proposed framework enables multiple cloud environments, enterprise branches, and distributed network nodes to collaboratively train machine learning models without transferring their raw security data to a centralized repository. Federated optimization techniques are employed to aggregate locally trained model parameters while maintaining data confidentiality. Adaptive analytics mechanisms continuously analyze network traffic, authentication activities, access patterns, and system behavior to identify emerging attacks and anomalous communication. The framework incorporates intelligent threat classification, behavioral anomaly detection, and adaptive model updates to improve detection capabilities against evolving cyber threats. Privacy-enhancing techniques, including secure aggregation and differential privacy, are considered to strengthen protection during model collaboration. Performance is evaluated using detection accuracy, precision, recall, F1-score, false-positive rate, communication overhead, and privacy effectiveness. The proposed approach aims to improve intrusion detection reliability, reduce sensitive data exposure, and support scalable cybersecurity management across heterogeneous enterprise cloud networks while maintaining operational efficiency and regulatory compliance.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 8 |
| Number | 6 |
| Pages | 15705-15713 |
| Published | 2024-11-12 |
| Issue | Vol. 8 No. 6 (2024): International Research Journal of Innovative Engineering (IRJIE) |
References
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35. Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
2. Bandari, U., & Thilagaraj, S. V. (2023). Defensive and offensive data strategy in manufacturing M&A: A comparative evaluation of data lake, data fabric, and data mesh integration patterns. International Journal of Research and Applied Innovations, 6(1), 8403–8413.
3. Sugumar, R. (2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12528.
4. Chandran, A. S. (2023). A unified Spark ETL stack across Cloudera CDP, Azure HDInsight, and Databricks. International Journal of Future Innovative Science and Technology, 6(6), 11769–11779.
5. Sahu, S. K. (2022). Automating insurance benefit interpretation: A domain-intelligence pipeline for document extraction, benefit classification, and plan recommendation. International Journal of Research and Applied Innovations, 5(6), 8252–8259.
6. Ahuja, D. (2023). Multi-cloud management strategies: Tools, challenges, and implementation methods. International Journal of Science Research in Computer Science Engineering and Information Technology, 9(10), 493–506.
7. Jinna, K. R., & Subramani, S. K. (2022). Supply-chain-aware CI/CD for regulated enterprises: Integrating artifact signing, secret injection and promotion gates in Kubernetes-native pipelines. International Journal of Computer Technology and Electronics Communication, 5(6), 16275–16285.
8. Mole, M. (2024). Modern business intelligence: Big data analytics and artificial intelligence for creating the data-driven value. International Research Journal of Innovative Engineering, 8(4), 15010–15022.
9. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285–2287.
10. Raja, G. V. (2022). AI-Driven Enterprise Architecture Integrating Zero-Trust Security for Secure Scalable and Compliant Cloud-Native Platforms. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12178.
11. Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
12. Kannan, K. S., Sunitha, G., Deepa, S. N., Babu, D. V., & Avanija, J. (2022). A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms. Computers and Electrical Engineering, 102, 108225.
13. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533–5537.
14. Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12179–12190.
15. 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.
16. Selvarajan, K. (2022). Architecting scalable self-service data platforms for enterprise analytics. International Journal of Science, Research and Technology, 5(2), 7427–7436.
17. Gadige, C. D. (2023). Scalable CRM automation for high-volume customer onboarding: An event-driven Salesforce architecture. International Journal of Emerging Trends in Engineering and Management Research, 8(6), 14772.
18. Kommineni, R. (2022). Monitoring as code for self-hosted Elasticsearch: Independent, delta-based lifecycle management of alerting watchers and dashboards across multi-cloud environments. International Journal of Science, Research and Technology, 5(2), 7437–7446.
19. Kopparthi, G. S. (2022). AI in healthcare: Unlocking the potential of predictive analytics. Journal of Computational Analysis and Applications, 30(2), 1183–1193. https://eudoxuspress.com/index.php/pub/article/view/5508
20. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Science, Research and Technology, 4(3), 5731–5744.
21. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162–8169.
22. Natta, P. K. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969.
23. Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15406.
24. 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.
25. Karrothu, A. (2024). Automated risk-based security controls for vulnerability detection and prevention across cloud-native software development lifecycles. International Research Journal of Innovative Engineering, 8(4), 14994–15009.
26. Shaik, N. (2024). Right-sizing at scale: A data-driven approach to Kubernetes cost optimization. International Journal of Future Innovative Science and Technology, 7(1), 12068–12078.
27. Bandhela, R. R., & Kannan, V. (2021). Leveraging generative AI and large language models for secure and efficient healthcare data management. Journal of Informatics Education and Research, 1(3).
28. Vemireddy, S. (2022). Architecting personalized tax advisory experiences through event-driven customer data integration. International Journal of Research Publications in Engineering, Technology and Management, 5(6), 7923–7929.
29. Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970–8977.
30. Karuturi, Y. (2023). Energy-aware capacity governance for large-scale SAP HANA deployments. International Journal of Research and Applied Innovations, 6(3), 6000–6009.
31. Randhi, K. S. (2021). Design and implementation of an API-driven hybrid integration framework for real-time utility inventory systems. International Journal of Emerging Trends in Engineering and Management Research, 6(2), 9281–9294.
32. Kalakoti, M. K. R. (2023). Interference-aware resource scheduling for cloud microservices on Kubernetes. International Journal of Future Innovative Science and Technology, 6(1), 9900–9909.
33. Puli, S. K. (2023). A fail-closed architecture for cryptographically enforced recipient authorization in clinical trial data delivery. International Journal of Information Technology and Management Information Systems (IJITMIS), 14(1), 148–164.
34. Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(3), 11812–11823.
35. Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
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