  Modern vehicles, including autonomous vehicles and connected vehicles, have
adopted an increasing variety of functionalities through connections and
communications with other vehicles, smart devices, and infrastructures.
However, the growing connectivity of the Internet of Vehicles (IoV) also
increases the vulnerabilities to network attacks. To protect IoV systems
against cyber threats, Intrusion Detection Systems (IDSs) that can identify
malicious cyber-attacks have been developed using Machine Learning (ML)
approaches. To accurately detect various types of attacks in IoV networks, we
propose a novel ensemble IDS framework named Leader Class and Confidence
Decision Ensemble (LCCDE). It is constructed by determining the best-performing
ML model among three advanced ML algorithms (XGBoost, LightGBM, and CatBoost)
for every class or type of attack. The class leader models with their
prediction confidence values are then utilized to make accurate decisions
regarding the detection of various types of cyber-attacks. Experiments on two
public IoV security datasets (Car-Hacking and CICIDS2017 datasets) demonstrate
the effectiveness of the proposed LCCDE for intrusion detection on both
intra-vehicle and external networks.
