Bousalem, Badre, Alalyan, Fahdah, Silva, Vinicius F., Cherrier, Sylvain, Jaafar, Wael et Langar, Rami.
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, vol. 4.
929–949.
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Résumé
Wireless communication technology is a vital enabler for 5G vehicle-to-everything (5G-V2X) communications, which are vulnerable to cyberattacks such as distributed denial of service (DDoS). DDoS detection requires sophisticated artificial intelligence (AI)-based approaches, whose main challenge is data privacy. Secured peer-to-peer (P2P) federated learning (FL) techniques could overcome this challenge by training detection models locally while sharing model parameters for secure aggregation. However, such a strategy scales poorly and incurs a high communication cost as the number of FL clients grows. In such a context, we propose SAFE-ADVENT, a secure P2P FL strategy that reduces the communication cost, incorporating transfer learning and client selection within the P2P FL system to detect DDoS attacks. By using data collected from our 5G-V2X testbed, under offline scenarios, SAFE-ADVENT converged faster than deep learning, centralized FL and SAC-based P2P FL benchmarks. Through live traffic testing and under adversarial attacks, SAFE-ADVENT outperformed all benchmarks in most scenarios. Finally, SAFE-ADVENT proved to be scalable by preserving high performance when increasing the number of FL agents.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Chercheur(-euse): | Chercheur(-euse) Jaafar, Waël Langar, Rami |
| Affiliation: | Génie logiciel et des technologies de l'information, Génie logiciel et des technologies de l'information |
| Date de dépôt: | 29 juin 2026 17:28 |
| Dernière modification: | 25 sept. 2026 23:44 |
| URI: | https://espace2.etsmtl.ca/id/eprint/33836 |
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