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Cyber Secure Digital Infrastructure through AI Analytics and Cloud-Based Security Intelligence

Mohanaad Shakir

College of Engineering Technology, University of Al-Maarif, Ramady, Iraq

Abstract

Digital infrastructure has become fundamental to modern organizations, supporting communication, financial transactions, healthcare services, industrial operations, government activities, cloud applications, and critical business processes. However, the rapid expansion of interconnected devices, cloud platforms, remote services, application programming interfaces, and distributed networks has significantly increased the cyberattack surface. Conventional security mechanisms based largely on static rules, signatures, and manual monitoring are increasingly challenged by sophisticated attacks that can evade traditional detection methods. Artificial intelligence (AI), advanced analytics, and cloud-based security intelligence offer an opportunity to strengthen digital infrastructure by enabling continuous monitoring, automated threat detection, predictive risk analysis, and adaptive security responses. This study examines the role of AI analytics and cloud-based security intelligence in developing secure, resilient, and adaptive digital infrastructure. The proposed approach integrates security logs, network traffic, endpoint telemetry, authentication records, application events, and cloud activity into a centralized analytical environment. Machine-learning techniques are subsequently applied to detect anomalies, classify threats, identify suspicious behavior, and predict emerging security risks. A mixed experimental methodology is proposed to compare AI-enabled security with conventional security monitoring under normal and simulated attack conditions. Key evaluation measures include detection accuracy, false-positive rate, detection latency, response time, resource utilization, and resilience following security incidents. The study emphasizes that effective cybersecurity requires not only advanced algorithms but also reliable data, secure cloud architecture, explainability, governance, privacy protection, and human oversight. The research aims to demonstrate how AI-driven cloud security intelligence can improve the visibility, responsiveness, and resilience of contemporary digital infrastructure

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 9 No. 6 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2024.0906001

Pages

16731-16741

Published

December 23, 2024
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mohanaad Shakir (%2024). Cyber Secure Digital Infrastructure through AI Analytics and Cloud-Based Security Intelligence. International Journal of Emerging Trends in Engineering and Management Research , Vol. 9 No. 6 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 16731-16741. https://doi.org/10.15662/ijetemr.2024.0906001

References

1. Kaur, K., et al. (2020). Q-learning based dynamic task scheduling for energy-efficient cloud computing. Future Generation Computer Systems, 108, 361–371. https://doi.org/10.1016/j.future.2020.02.018
2. 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.
3. Pasumarthi, H. (2024). Engineering Large-Scale WMS Integrations: A Practical Guide to Implementing Blue Yonder with IBM ACE, Datapower, MQ, and SAP. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10008-10016.
4. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
5. Chaba, A. (2023). A scalable real-time customer data platform architecture for cross-channel enterprise personalization. International Journal of Research and Applied Innovations, 6(1), 8392–8396.
6. Soundappan, S. J. (2022). Integrated Risk Governance Framework for Financial Compliance Supply Chain Resilience and Enterprise Data Management. International Journal of Computer Technology and Electronics Communication, 5(6), 16254-16263.
7. Suddala, V. R. A. K. (2024). Machine learning for operational excellence: Real-world applications. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13917.
8. Reddy, G. C. P. (2019). Asymptotic Analysis in Cloud Resource Allocation: Scalability of Virtual Machines, Scheduling Complexity, and Auto-Scaling Mechanisms. Journal of Computational Analysis and Applications, 27(7).
9. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.
10. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453-7461.
11. Hoque, M. J., Hasan, M. M., Khatun, M. M., Akter, F., & Mohammad, A. R. (2021). Impact of COVID-19 on Consumer Buying Behavior During COVID-19 Pandemic Using Data Analytics. Journal of Business and Management Studies, 3(2), 296-307.
12. Bandaru, P. K. (2023). Validation methodologies for over-the-air software updates in modern automotive platforms. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8159–8165.
13. Mishu, K. P., Papri, N. K., Sarker, A., Supti, A. M., Ahmed, M. T., Mahmood, M. M., & Islam, M. R. (2023). Advancing US critical industry supply chain through AI and machine learning-driven logistics, risk mitigation, and operational excellence. Cuestiones de Fisioterapia, 52(3), 971–977.
14. Gujarathi, M. (2022). Parallel verification architecture for low-latency enterprise decision systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4640-4644.
15. Beeram, S. (2024). AI-driven intelligent traffic management in Microsoft Azure: Predictive routing for resilient cloud applications. International Journal of Advanced Engineering Science and Information Technology, 7(4), 14534–14538.
16. Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Science, Research and Technology (IJSRAT), 7(3), 12085–12101.
17. Reddy, B. P. (2021). Optimizing smart grid performance using Machine Learning: A Data-Driven Approach to Energy Efficiency. J. Electrical Systems, 17(4), 149-156.
18. Chandra Shekhar, P. (2021). Next-Gen Test Automation in Life Insurance: Self-Healing Frameworks. International Journal of Scientific Research in Engineering and Management, 5(7), 1-13.
19. Rajula, A. (2024). Replication-aware caching for low-latency clinical knowledge retrieval. International Journal of Computer Technology and Electronics Communication, 7(6), 9997-10007.
20. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
21. Yepuri, V. K., Polamarasetty, V. K., Donthi, S., & Gondi, A. K. R. (2023). Containerization of a polyglot microservice application using Docker and Kubernetes.arXiv preprint arXiv:2305.00600
22. Narapareddy, V. S. R., & Yerramilli, S. K. (2023). Artificial intelligence incident forecasting. International Journal of Engineering Technology Research & Management, 7(12), 551–559.
23. 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.
24. Chy, M. S. K., Abdullah, S. M., Nabil, M. A., Alam, A., Himeluzzaman, M., Onik, T. A., ... & Khan, H. A. (2022). QShield-NS: A Variational Quantum Machine Learning Model for Zero-Day Cyber Threat Detection in National Security Systems. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9283.
25. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
26. Vemireddy, S. (2023). Building resilient enterprise platforms using event-driven and distributed computing models. International Journal of Research and Applied Innovations (IJRAI), 6(6), 10106–10112.
27. Padmanabham, S. (2023). Building resilient banking platforms using event-driven microseconds and cloud-native architecture. International Journal of Research and Applied Innovations, 6(4), 9284–9290.
28. Raj, A. A., & Sugumar, R. (2022, October). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.
29. Koganti, H. (2024). Beyond reactive scaling: A review of AI-driven proactive and context-aware auto-scaling in cloud-edge environments. International Journal of Engineering & Extended Technologies Research, 6(3), 8175–8183.
30. Rahul Reddy Bandhela, RamMohan Reddy Kundavaram. (2023). A Comparative Study on Neural Network Architectures for Image Recognition Applications . Journal of Computational Analysis and Applications (JoCAAA), 31(1), 1334–1342. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3566
31. Gopinathan, V. R., & Mali, R. K. (2022). Building cognitive technology frameworks through artificial intelligence SAP cloud automation and enterprise intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7152-7162.
32. Challa, R. (2023). Reliability Engineering for Zero-Downtime Integration of HPC Systems into Regulated Environments. International Journal of Research and Applied Innovations, 6(6), 10082-10092.
33. 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.
34. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
35. Liang, X., et al. (2020). Intelligent task prediction and computation offloading based on mobile-edge cloud computing. Future Generation Computer Systems, 102, 925–931. https://doi.org/10.1016/j.future.2019.09.035