LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles
This work addresses security vulnerabilities in IoV systems, which is critical for protecting connected and autonomous vehicles, but it is incremental as it builds on existing ML methods.
The authors tackled the problem of detecting various cyber-attacks in Internet of Vehicles (IoV) networks by proposing LCCDE, an ensemble intrusion detection framework that selects the best-performing ML model per attack class, achieving effective detection on two public datasets.
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.