A Cloud Security Framework for AI-Driven Network Defense: Overcoming AI Integration Barriers Using MFA, Multivariate Threat Classification, and Semantic Precedent Retrieval

Sebastian Otto Falkenstern

doi:10.15662/IJRAI.2021.0406012

Authors

Sebastian Otto Falkenstern Cybersecurity Analyst, Munich, Germany Author

DOI:

https://doi.org/10.15662/IJRAI.2021.0406012

Keywords:

Cloud security, AI-driven network defense, Artificial intelligence integration, Multi-factor authentication, Multivariate threat classification, Semantic Precedent Retrieval, Anomaly detection, Threat intelligence, Identity management, Cybersecurity automation, Real-time monitoring, Adaptive defense, Cloud-native security, Predictive analytics, Risk mitigation

Abstract

The proliferation of cloud computing and AI technologies has amplified the need for robust and adaptive network security mechanisms. This paper proposes a cloud security framework for AI-driven network defense designed to address key challenges in integrating artificial intelligence into security workflows. The framework incorporates multi-factor authentication (MFA) to ensure secure identity verification and mitigate unauthorized access across distributed cloud environments. Multivariate threat classification models are utilized to analyze multidimensional network and behavioral data, enabling precise detection of anomalies and potential cyber attacks. Furthermore, a Semantic Precedent Retrieval module enhances decision-making by referencing historical threat patterns and policy precedents to optimize response strategies. The proposed framework supports real-time monitoring, automated remediation, and improved situational awareness, demonstrating enhanced detection accuracy and reduced false positives. This approach provides a scalable, intelligent foundation for securing cloud networks against advanced threats while facilitating seamless AI integration.

References

1. Hinton, G., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. https://doi.org/10.1126/science.1127647

2. 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.

3. Devi, J. S., & Sugumar, R. (2014). Host Based Intrusion Detection to Prevent Virtual Network System from Intruders in Cloud. International Journal of Science and Research (IJSR).

4. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.

5. Hardial Singh, “ENHANCING CLOUD SECURITY POSTURE WITH AI-DRIVEN THREAT DETECTION AND RESPONSE MECHANISMS”, INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), VOLUME-6, ISSUE-2, 2019.

6. Thangavelu, K., Sethuraman, S., & Hasenkhan, F. (2021). AI-Driven Network Security in Financial Markets: Ensuring 100% Uptime for Stock Exchange Transactions. American Journal of Autonomous Systems and Robotics Engineering, 1, 100-130.

7. Mather, T., Kumaraswamy, S., & Latif, S. (2009). Cloud security and privacy: An enterprise perspective on risks and compliance. O’Reilly Media.

8. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal ofComputer Science Applications and Information Technology, 5(1), 1-8.

9. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (NIST Special Publication 800-145). National Institute of Standards and Technology.

10. Navandar, P. (2021). Fortifying cybersecurity in Healthcare ERP systems: unveiling challenges, proposing solutions, and envisioning future perspectives. Int J Sci Res, 10(5), 1322-1325.

11. Kapadia, V., Jensen, J., McBride, G., Sundaramoothy, J., Deshmukh, R., Sacheti, P., & Althati, C. (2015). U.S. Patent No. 8,965,820. Washington, DC: U.S. Patent and Trademark Office.

12. Popović, K., & Hocenski, Ž. (2010). Cloud computing security issues and challenges. In Proceedings of the 33rd International Convention MIPRO (pp. 344–349). IEEE.

13. Sivaraju, P. S. (2021). 10x Faster Real-World Results from Flash Storage Implementation (Or) Accelerating IO Performance A Comprehensive Guide to Migrating From HDD to Flash Storage. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(5), 5575-5587.

14. Dhanorkar, T., Vijayaboopathy, V., & Das, D. (2020). Semantic Precedent Retriever for Rapid Litigation Strategy Drafting. Journal of Artificial Intelligence & Machine Learning Studies, 4, 71-109.

15. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.

16. Samarati, P., & de Capitani di Vimercati, S. (2001). Access control: Policies, models, and mechanisms. In R. Focardi & R. Gorrieri (Eds.), Foundations of security analysis and design (pp. 137–196). Springer. https://doi.org/10.1007/3-540-45608-2_3

17. Konidena, B. K., Bairi, A. R., & Pichaimani, T. (2021). Reinforcement Learning-Driven Adaptive Test Case Generation in Agile Development. American Journal of Data Science and Artificial Intelligence Innovations, 1, 241-273.

18. Arora, Anuj. "The Significance and Role of AI in Improving Cloud Security Posture for Modern Enterprises." International Journal of Current Engineering and Scientific Research (IJCESR), vol. 5, no. 5, 2018, ISSN 2393-8374 (Print), 2394-0697 (Online).

19. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.

20. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.

21. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.

22. Yu, S., Wang, C., Ren, K., & Lou, W. (2010). Achieving secure, scalable, and fine-grained data access control in cloud computing. Proceedings of IEEE INFOCOM, 1–9. https://doi.org/10.1109/INFCOM.2010.5462174