On the Security of Connected Vehicles: Intrusion Detection Using Federated Learning in the IoV

The expansion of Internet of Vehicles (IoV) technology brings significant cybersecurity and privacy concerns. This research study addresses cybersecurity challenges within the IoV and Intelligent Transportation System (ITS). It explores machine learning applications for detecting and preventing cyberattacks on the IoV network and autonomous vehicles, with the goal of developing and evaluating algorithms tailored to enhance IoV security, intrusion detection, and privacy. In this paper, we adopted a clustered machine learning model using Federated Learning and Convolutional Neural Networks (CNN) for IoV intrusion detection. Different scenarios involving centralized and decentralized clustering schemes are introduced. The proposed federated learning intrusion detection model has achieved a testing accuracy of 97.54 %, precision of 97.01 %, recall of 98.12 %, and an F1-score of 0.976.