Enhancing Zero Trust Cybersecurity using Machine Learning and Deep Learning Approaches
Article Sidebar
Main Article Content
Abstract
The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenges in detecting sophisticated and developing cyber threats, particularly due to its trust on traditional security mechanisms that struggle to manage internal threats and sophisticated attack techniques. To report these shortcomings, the proposed study discovers the combination of advanced machine learning (ML) and deep learning (DL) performances to improve the anomaly detection proficiencies within ZTA environments. The study develops the CICIDS2017 dataset, which contains diverse and realistic network traffic patterns, to assess the efficiency of nine different models: Naïve Bayes, Logistic Regression, Random Forest, Decision Tree, Gated Recurrent Unit (GRU), Multi-layer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Convolutional Neural Network (CNN). Concluded comprehensive investigation and performance evaluation, the study validates that ensemble methods such as Random Forest and Decision Tree, together with deep learning models like LSTM and GRU, significantly exceed conventional models in terms of accuracy and detection abilities. The best-performing models attained up to 99.99% accuracy in recognizing malicious network activity. This exceptional performance validates that the strong potential of participating intelligent learning-based methods into ZTA to create scalable and dynamic security solutions with high accuracy. These findings illustrate the value of ML/DL in enhancing the threat detection layer of ZTA, eventually providing a stronger resistance to advanced attacks cyber threats.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
N. F. Syed, S. W. Shah, A. Shaghaghi, A. Anwar, Z. Baig, and R. Doss, “Zero Trust Architecture (ZTA): A comprehensive survey,” IEEE Access, vol. 10, pp. 57143–57179, 2022, doi: 10.1109/ACCESS.2022.3174679.
E. Bandara, X. Liang, S. Shetty, R. Mukkamala, A. Rahman, and N. W. Keong, “Skunk - A blockchain and zero trust security enabled federated learning platform for 5G/6G network slicing,” in 2022 Annual IEEE Communications Society Conference on Sensor, Mesh, and Ad Hoc Communications and Networks (SECON), 2022, pp. 109–117. doi: 10.1109/SECON55815.2022.9918536.
S. Li, M. Iqbal, and N. Saxena, “Future industry Internet of Things with Zero-trust security,” Information Systems Frontiers, 2022, doi: 10.1007/s10796-021-10199-5.
M. Alioto, “Aggressive design reuse for ubiquitous zero-trust edge security—From physical design to machine-learning-based hardware patching,” IEEE Open Journal of the Solid-State Circuits Society, vol. 3, pp. 1–16, 2022. doi: 10.1109/OJSSCS.2022.3223274.
H. A. Kholidy et al., “Toward zero trust security in 5G open architecture network slices,” in 2022 IEEE Military Communications Conference (MILCOM), 2022, pp. 577–582. doi: 10.1109/MILCOM55135.2022.10017474.
H. Sedjelmaci and N. Ansari, “Zero trust architecture empowered attack detection framework to secure 6G edge computing,” IEEE Network, vol. 38, no. 1, pp. 196–202, 2024. doi: 10.1109/MNET.131.2200513.
K. A. Abuhasel, “A zero-trust network-based access control scheme for sustainable and resilient industry 5.0,” IEEE Access, vol. 11, pp. 116398–116409, 2023. doi: 10.1109/ACCESS.2023.3325879.
S. Ouiazzane, M. Addou, and F. Barramou, “A zero-trust model for intrusion detection in drone networks,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 11, pp. 525–537, 2023. doi: 10.14569/IJACSA.2023.0141154.
A. Z. Alalmaie, P. Nanda, and X. He, “ZT-NIDS: Zero trust, network intrusion detection system,” in 2023 International Conference on Security and Cryptography (SECRYPT), 2023, pp. 99–110. doi: 10.5220/0012080000003555.
T. Sasada, M. Kawai, Y. Masuda, Y. Taenaka, and Y. Kadobayashi, “Factor analysis of learning motivation difference on cybersecurity training with zero trust architecture,” IEEE Access, vol. 11, pp. 141358–141374, 2023. doi: 10.1109/ACCESS.2023.3341093.
E. C. Nkoro, J. N. Njoku, C. I. Nwakanma, J. M. Lee, and D. S. Kim, “Zero-trust marine cyberdefense for IoT-based communications: An explainable approach,” Electronics, vol. 13, no. 2, p. 276, 2024. doi: 10.3390/electronics13020276.
Y. Cao, S. R. Pokhrel, Y. Zhu, R. Doss, and G. Li, “Automation and orchestration of zero trust architecture: Potential solutions and challenges,” Machine Intelligence Research, vol. 21, no. 2, pp. 294–317, 2024. doi: 10.1007/s11633-023-1456-2.
S. A. Khowaja, P. Khuwaja, K. Dev, K. Singh, L. Nkenyereye, and D. Kilper, “ZETA: Zero-trust attack framework with split learning for autonomous vehicles in 6G networks,” in 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024. doi: 10.1109/WCNC57260.2024.10571158.
V. Sobchuk and O. Barabash, “Sequential intrusion detection system for zero-trust cyber defense of IoT/IIoT networks,” Journal of Cyber Security and Information Technologies, no. 3, 2024. doi: 10.20998/2522-9052.2024.3.11.
Y. Liu, Z. Su, H. Peng, Y. Xiang, W. Wang, and R. Li, “Zero trust-based mobile network security architecture,” IEEE Wireless Communications, vol. 31, no. 2, pp. 82–88, 2024. doi: 10.1109/MWC.001.2300375.
E. Ogendi, “Leveraging advanced cybersecurity analytics to reinforce zero-trust architectures within adaptive security frameworks,” International Journal of Research Publication and Reviews, vol. 6, no. 2, pp. 729–742, 2025. doi: 10.55248/gengpi.6.0225.0729.
S. Ahmadi, “Autonomous identity-based threat segmentation in zero trust architectures,” 2025.
B. T. Ofili, E. O. Erhabor, and O. T. Obasuyi, “Enhancing federal cloud security with AI: Zero trust, threat intelligence and CISA compliance,” World Journal of Advanced Research and Reviews, vol. 25, no. 2, pp. 620–635, 2025. doi: 10.30574/wjarr.2025.25.2.0620.
C. S. Ravi, M. Shaik, V. Saini, S. Chitta, V. Sri, and M. Bonam, “Beyond the firewall: Implementing zero trust with network microsegmentation,” 2025.
S. Shakya, R. Abbas, and S. Maric, “A novel zero-touch, zero-trust, AI/ML enablement framework for IoT network security,” arXiv, 2025. doi: 10.48550/arXiv.2502.03614.
Y. H.-S. Kam, K. Jones, R. Rawlinson-Smith, and K. Tam, “In search of suitable methods for cost-benefit analysis of cyber risk mitigation in offshore wind: A survey,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 314–328, Oct. 2024. doi: 10.33093/jiwe.2024.3.3.20.
S. T. Jimoh and S. S Al-Juboori, “Cyber-securing medical devices using machine learning: A case study of pacemaker,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 271–289, Oct. 2024. doi: 10.33093/jiwe.2024.3.3.17.
A. K. A. Razack and M. F. M. Saad, “Enhancing cybersecurity awareness through gamification: Design an interactive cybersecurity learning platform for multimedia university students,” Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 21–40, Oct. 2024. doi: 10.33093/jiwe.2024.3.3.2.