Federated learning based IDS approach for the IoV

Abstract

The Internet of Vehicles (IoV) is an Internet of Things (IoT) application that offers several utilities such as traffic analysis, safe driving, road optimization, and travel comfort. Software-Defined Networking (SDN) technology has been shown to provide various benefits to support the IoV. However, the construction of IoV makes it a complex system posing several challenges among which the important ones are security and privacy of data. Intrusion Detection Systems (IDSs) have been proposed in the IoV to identify cyber attacks and protect private data. Recently work has started to implement IDSs based on Federated learning as collaborative IDSs have proved effective security of IoV. In another hand, trust management has revolutionized the IoV filed, providing decision-making support to secure the network. Stating that an SDN-driven IoV architecture in which nodes trustworthiness gets assessed can provide a promising framework for IDS, we propose in this paper a Federated learning-based IDS for the IoV under the SDN structure. We integrate trust metrics to assist in securing the IoV network. Simulation experiments are conducted to validate the proposal.