SAFE-ADVENT: Privacy-Preserving and Resilient Distributed Federated Learning for DDoS Attacks Detection in 5G-V2X Networks
- DOI
- 10.1109/tmlcn.2026.3697341
- Published
- 2026
- Container
- IEEE Transactions on Machine Learning in Communications and Networking
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tmlcn.2026.3697341,
title = {SAFE-ADVENT: Privacy-Preserving and Resilient Distributed Federated Learning for DDoS Attacks Detection in 5G-V2X Networks},
author = {Badre Bousalem and Fahdah Alalyan and Vinicius F. Silva and Sylvain Cherrier and Wael Jaafar and Rami Langar},
year = {2026},
journal = {IEEE Transactions on Machine Learning in Communications and Networking},
doi = {10.1109/tmlcn.2026.3697341},
url = {https://doi.org/10.1109/tmlcn.2026.3697341}
}RIS
TY - JOUR TI - SAFE-ADVENT: Privacy-Preserving and Resilient Distributed Federated Learning for DDoS Attacks Detection in 5G-V2X Networks AU - Badre Bousalem AU - Fahdah Alalyan AU - Vinicius F. Silva AU - Sylvain Cherrier AU - Wael Jaafar AU - Rami Langar PY - 2026 JO - IEEE Transactions on Machine Learning in Communications and Networking DO - 10.1109/tmlcn.2026.3697341 UR - https://doi.org/10.1109/tmlcn.2026.3697341 ER -
APA
Bousalem, B., Alalyan, F., Silva, V. F., Cherrier, S., Jaafar, W., & Langar, R. (2026). SAFE-ADVENT: Privacy-Preserving and Resilient Distributed Federated Learning for DDoS Attacks Detection in 5G-V2X Networks. IEEE Transactions on Machine Learning in Communications and Networking. https://doi.org/10.1109/tmlcn.2026.3697341
Source records
- crossref · retrieved 2026-11-10T07:40:52.418Z