6533b7d0fe1ef96bd125b538

RESEARCH PRODUCT

On-Demand Security Framework for 5GB Vehicular Networks

subject

description

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 trustedinteractions 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.