Briliyant, O.  ORCID: https://orcid.org/0000-0002-1054-8112, Setiadji, M. Y. B., Javed, A. ORCID: https://orcid.org/0000-0001-9761-0945 and Cherdantseva, Y. ORCID: https://orcid.org/0000-0002-3527-1121
2025.
Federated learning for collaborative cyber-attack detection in Internet of Vehicles (IoV) network.
Presented at: 8th International Conference on Advanced Communication Technologies and Networking (CommNet),
Rabat, Morocco,
3-5 December 2025.
2025 8th International Conference on Advanced Communication Technologies and Networking (CommNet).
IEEE,
10.1109/CommNet68224.2025.11288882
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Abstract
The Internet of Vehicles (IoV) enhances modern transportation but introduces significant cyber security vulnerabilities and privacy risks. Traditional centralized Intrusion Detection Systems (IDS) are inadequate for this environment, as they could compromise driver privacy. This study proposes a novel federated learning (FL) framework that enables collaborative cyber-attack detection across vehicles without sharing private data. We implemented and evaluated advanced FL algorithms (FedAvg, FedProx, SCAFFOLD, and FedNova) with a deep neural network (DNN) architecture. The framework is tested under a realistic non-identically and independently distributed (non-IID) data, where the federated clients are designed to detect specific in-vehicle Controller Area Network (CAN) attacks. Our experimental validation demonstrates that FedNova achieved an optimal F1-score of 81.67% for attack detection while maintaining complete data isolation. These findings establish that FL has the potential to be used as an IDS model in the IoV networks, while still preserving user privacy.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | IEEE |
| ISBN: | 979-8-3315-5781-2 |
| ISSN: | 2771-7402 |
| Last Modified: | 19 Jan 2026 16:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/183945 |
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