Integrating Machine Learning with Zero Trust Principles for Real-Time Threat Detection and Response

References

[1] Gudala, L., Shaik, M., & Venkataramanan, S. (2021). Leveraging machine learning for enhanced threat detection and response in zero trust security frameworks: An Exploration of Real-Time Anomaly Identification and Adaptive Mitigation Strategies. Journal of Artificial Intelligence Research, 1(2), 19-45.

[2] Tiwari, S., Sarma, W., & Srivastava, A. (2022). Integrating artificial intelligence with zero trust architecture: Enhancing adaptive security in modern cyber threat landscape. International Journal of Research and Analytical Reviews, 9, 712-728.

[3] Ejeofobiri, C. K., Adelere, M. A., & Shonubi, J. A. (2022). Developing adaptive cybersecurity architectures using Zero Trust models and AI- powered threat detection algorithms. Int J Comput Appl Technol Res, 11(12), 607-621.

[4] Paul, E. M., Mmaduekwe, U., Kessie, J. D., & Dolapo, M. (2024). Zero trust architecture and AI: A synergistic approach to next-generation cybersecurity frameworks. International Journal of Science and Research Archive, 13(2), 4159-4169.

[5] Joshi, H. (2024). Emerging technologies driving zero trust maturity across industries. IEEE Open Journal of the Computer Society.

[6] Ike, C. C., Ige, A. B., Oladosu, S. A., Adepoju, P. A., Amoo, O. O., & Afolabi, A. I. (2021). Redefining zero trust architecture in cloud networks: A conceptual shift towards granular, dynamic access control and policy enforcement. Magna Scientia Advanced Research and Reviews, 2(1), 074-086.

[7] Syed, N. F., Shah, S. W., Shaghaghi, A., Anwar, A., Baig, Z., & Doss, R. (2022). Zero trust architecture (zta): A comprehensive survey. IEEE access, 10, 57143-57179.

[8] Alevizos, L., Ta, V. T., & Hashem Eiza, M. (2022). Augmenting zero trust architecture to endpoints using blockchain: A state‐of‐the‐art review. Security and privacy, 5(1), e191.

[9] ARAMIDE, O. O. (2024). Zero-trust identity principles in next-gen networks: AI-driven continuous verification for secure digital ecosystems. World Journal of Advanced Research and Reviews, 23, 3304-3316.

[10] Sarkar, S., Choudhary, G., Shandilya, S. K., Hussain, A., & Kim, H. (2022). Security of zero trust networks in cloud computing: A comparative review. Sustainability, 14(18), 11213.

[11] Okoli, U. I., Obi, O. C., Adewusi, A. O., & Abrahams, T. O. (2024). Machine learning in cybersecurity: A review of threat detection and defense mechanisms. World Journal of Advanced Research and Reviews, 21(1), 2286- 2295.

[12] Chen, B., Qiao, S., Zhao, J., Liu, D., Shi, X., Lyu, M., ... & Zhai, Y. (2020). A security awareness and protection system for 5G smart healthcare based on zero -trust architecture. IEEE internet of things journal, 8(13), 10248-10263.

[13] Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023). Explainable intrusion detection for cyber defences in the internet of things: Opportunities and solutions. IEEE Communications Surveys & Tutorials, 25(3), 1775-1807.

[14] Stafford, V. (2020). Zero trust architecture. NIST special publication, 800(207), 800-207.

[15] Potluri, S. (2024). A Zero Trust-Based Identity and Access Management Framework for Cross- Cloud Federated Networks. International Journal of Emerging Research in Engineering and Technology, 5(2), 28-40.

[16] Kodakandla, N. (2024). Securing cloud-native infrastructure with Zero Trust Architecture. Journal of Current Science and Research Review, 2(02), 18-28.

[17] Kang, H., Liu, G., Wang, Q., Meng, L., & Liu, J. (2023). Theory and application of zero trust security: A brief survey. Entropy, 25(12), 1595.

[18] Syed, A. A. M. (2024). Zero Trust Security in Hybrid Cloud Environments: Implementing and Evaluating Zero Trust Architectures in AWS and On-Premise Data Centers. International Journal of Emerging Trends in Computer Science and Information Technology, 5(2), 42- 52.

[19] Iyer, K. I. (2021). From Signatures to Behavior: Evolving Strategies for Next-Generation Intrusion Detection. European Journal of Advances in Engineering and Technology, 8(6), 165-171.

[20] Ejiofor, O. E. (2023). A comprehensive framework for strengthening USA financial cybersecurity: integrating machine learning and AI in fraud detection systems. European Journal of Computer Science and Information Technology, 11(6), 62-83.

[21] Ojika, F. U., Owobu, W. O., Abieba, O. A., Esan, O. J., Ubamadu, B. C., & Daraojimba, A. I. (2024). The Role of AI in Cybersecurity: A Cross-Industry Model for Integrating Machine Learning and Data Analysis for Improved Threat Detection. Comput Secur.[Year].

[22] Chukwunweike, J. N., Praise, A., & Bashirat, B. A. (2024). Harnessing Machine Learning for Cybersecurity: How Convolutional Neural Networks are Revolutionizing Threat Detection and Data Privacy.

[23] Alonge, E. O., Eyo-Udo, N. L., Ubanadu, B. C., Daraojimba, A. I., Balogun, E. D., & Ogunsola, K. O. (2021). Enhancing data security with machine learning: A study on fraud detection algorithms. Journal of Data Security and Fraud Prevention, 7(2), 105-118.

[24] Schmitt, M. (2023). Securing the digital world: Protecting smart infrastructures and digital industries with artificial intelligence (AI)- enabled malware and intrusion detection. Journal of Industrial Information Integration, 36, 100520.

[25] Mohammed, A. (2023). AI and Machine Learning in Cybersecurity: Strategies, Threats, and Exploits. Innovative Computer Sciences Journal, 9(1).

[26] Alonge, E. O., Eyo-Udo, N. L., Ubanadu, B. C., Daraojimba, A. I., Balogun, E. D., & Ogunsola, K. O. (2021). Enhancing data security with machine learning: A study on fraud detection algorithms. Journal of Data Security and Fraud Prevention, 7(2), 105-118.

[27] Afaq, A., Haider, N., Baig, M. Z., Khan, K. S., Imran, M., & Razzak, I. (2021). Machine learning for 5G security: Architecture, recent advances, and challenges. Ad Hoc Networks, 123, 102667.

[28] Nyamasvisva, T. E., & Arabi, A. A. M. (2022). A comprehensive SWOT analysis for Zero Trust network security model. International Journal of Infrastructure Research and Management Vol. 10 (1), June 2022.

[29] Mayeke, N. R., Arigbabu, A. T., Olaniyi, O. O., Okunleye, O. J., & Adigwe, C. S. (2024). Evolving access control paradigms: A comprehensive multi-dimensional analysis of security risks and system assurance in cyber engineering. Available at SSRN 4752902.

[30] DeCusatis, C., Liengtiraphan, P., Sager, A., & Pinelli, M. (2016, November). Implementing zero trust cloud networks with transport access control and first packet authentication. In 2016 IEEE International Conference on Smart Cloud (SmartCloud) (pp. 5-10). IEEE.

@article{1710384,
      author = {Mukul Mangla},
      title = {Integrating Machine Learning with Zero Trust Principles for Real-Time Threat Detection and Response},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {9},
      pages = {564-575},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710384.pdf},
      abstract = {The rapid advancement of cyber threats has rendered traditional perimeter-based security approaches insufficient, necessitating the development of adaptive and intelligent solutions. Zero Trust Architecture (ZTA), grounded in the principles of "never trust, always verify?, represents a paradigm shift that enforces continuous authentication, authorization, and least-privilege access across digital ecosystems (Stafford, 2020; Syed et al., 2022). Although ZTA enhances the security posture, its static policy enforcement mechanisms often face challenges in addressing real-time, high volume cyberattacks. Machine learning (ML), with its capabilities in anomaly detection, behavioral analysis, and predictive modelling, offers a dynamic layer that can augment ZTA for proactive and real-time threat detection (Gudula et al., 2021; Okoli et al., 2024). This study investigates the integration of ML techniques into Zero Trust principles to design a hybrid framework capable of continuous verification, adaptive response, and real-time anomaly mitigation. Utilizing benchmark cybersecurity datasets and advanced ML algorithms, the proposed framework demonstrates improvements in detection accuracy, scalability, and automated response latency over conventional models. These findings underscore the synergistic potential of combining ML with ZTA, establishing a pathway for next-generation cybersecurity frameworks applicable across cloud, IoT, and enterprise infrastructures (Paul et al., 2024; Tiwari et al., 2022). This study contributes to the advancement of secure digital ecosystems by proposing a holistic model that addresses both the strengths and limitations of current ML-augmented Zero Trust systems.},
      keywords = {Zero Trust Architecture, Machine Learning, Cybersecurity, Real-Time Threat Detection, Adaptive Security, Intrusion Detection, Automated Response},
      month = {March},
  }