Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN Environments.
- DOI
- 10.3390/s25061905
- Published
- 2025 Mar 19
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.3390/s25061905,
title = {Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN Environments.},
author = {Ashfaq F and Wasim M and Shah MA and Ahad A and Pires IM},
year = {2025},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s25061905},
url = {https://doi.org/10.3390/s25061905}
}RIS
TY - JOUR TI - Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN Environments. AU - Ashfaq F AU - Wasim M AU - Shah MA AU - Ahad A AU - Pires IM PY - 2025 JO - Sensors (Basel, Switzerland) DO - 10.3390/s25061905 UR - https://doi.org/10.3390/s25061905 ER -
APA
F, A., M, W., MA, S., A, A., & IM, P. (2025). Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN Environments.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s25061905
Source records
- pubmed · retrieved 2026-09-25T17:30:57.135Z