  Modern vehicles, including autonomous vehicles and connected vehicles, are
increasingly connected to the external world, which enables various
functionalities and services. However, the improving connectivity also
increases the attack surfaces of the Internet of Vehicles (IoV), causing its
vulnerabilities to cyber-threats. Due to the lack of authentication and
encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs)
are essential approaches to protect modern vehicle systems from network
attacks. In this paper, a transfer learning and ensemble learning-based IDS is
proposed for IoV systems using convolutional neural networks (CNNs) and
hyper-parameter optimization techniques. In the experiments, the proposed IDS
has demonstrated over 99.25% detection rates and F1-scores on two well-known
public benchmark IoV security datasets: the Car-Hacking dataset and the
CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for
cyber-attack detection in both intra-vehicle and external vehicular networks.
