Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics
- DOI
- 10.3389/frai.2026.1701944
- Published
- 2026-02-10
- Container
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/frai.2026.1701944,
title = {Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics},
author = {R. Shameli and Sujatha Rajkumar},
year = {2026},
journal = {Frontiers in Artificial Intelligence},
doi = {10.3389/frai.2026.1701944},
url = {https://doi.org/10.3389/frai.2026.1701944}
}RIS
TY - JOUR TI - Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics AU - R. Shameli AU - Sujatha Rajkumar PY - 2026 JO - Frontiers in Artificial Intelligence DO - 10.3389/frai.2026.1701944 UR - https://doi.org/10.3389/frai.2026.1701944 ER -
APA
Shameli, R., & Rajkumar, S. (2026). Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2026.1701944
Source records
- crossref · retrieved 2026-09-25T23:02:04.885Z