  Modern vehicles, including connected vehicles and autonomous vehicles,
nowadays involve many electronic control units connected through intra-vehicle
networks to implement various functionalities and perform actions. Modern
vehicles are also connected to external networks through vehicle-to-everything
technologies, enabling their communications with other vehicles,
infrastructures, and smart devices. However, the improving functionality and
connectivity of modern vehicles also increase their vulnerabilities to
cyber-attacks targeting both intra-vehicle and external networks due to the
large attack surfaces. To secure vehicular networks, many researchers have
focused on developing intrusion detection systems (IDSs) that capitalize on
machine learning methods to detect malicious cyber-attacks. In this paper, the
vulnerabilities of intra-vehicle and external networks are discussed, and a
multi-tiered hybrid IDS that incorporates a signature-based IDS and an
anomaly-based IDS is proposed to detect both known and unknown attacks on
vehicular networks. Experimental results illustrate that the proposed system
can detect various types of known attacks with 99.99% accuracy on the
CAN-intrusion-dataset representing the intra-vehicle network data and 99.88%
accuracy on the CICIDS2017 dataset illustrating the external vehicular network
data. For the zero-day attack detection, the proposed system achieves high
F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The
average processing time of each data packet on a vehicle-level machine is less
than 0.6 ms, which shows the feasibility of implementing the proposed system in
real-time vehicle systems. This emphasizes the effectiveness and efficiency of
the proposed IDS.
