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Article Dans Une Revue IEEE Internet of Things Magazine Année : 2023

On-Demand Security Framework for 5GB Vehicular Networks

Résumé

Building accurate Machine Learning (ML) at-tack detection models for 5G and Beyond (5GB) vehicular networks requires collaboration between Vehicle-to-Everything (V2X) nodes. However, while operating collaboratively, ensuring the ML model’s security and data privacy is challenging. To this end, this article proposes a secure and privacy-preservation on-demand framework for building attack-detection ML models for 5GB vehicular networks. The proposed framework emerged from combining 5GB technologies, namely, Federated Learning (FL), blockchain, and smart contracts to ensure fair and trusted interactions between FL servers (edge nodes) with FL workers (vehicles). Moreover, it also provides an efficient consensus algorithm with an intelligent incentive mechanism to select the best FL workers that deliver highly accurate local ML mod-els. Our experiments demonstrate that the framework achieves higher accuracy on a well-known vehicular dataset with a lower blockchain consensus time than related solutions. Specifically, our framework enhances the accuracy by 14% and decreases the consensus time, at least by 50%, compared to related works. Finally, this article discusses the framework’s key challenges and potential solutions.

Dates et versions

hal-04161066 , version 1 (13-07-2023)

Identifiants

Citer

Abdelwahab Boualouache, Bouziane Brik, Sidi-Mohammed Senouci, Thomas Engel. On-Demand Security Framework for 5GB Vehicular Networks. IEEE Internet of Things Magazine, 2023, 6 (2), pp.26-31. ⟨10.1109/IOTM.001.2200233⟩. ⟨hal-04161066⟩
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