CDiB-IDS: Contextual DistilBERT-Based Intrusion Detection System for IoV

With the rise of the Internet of Vehicles (IoV), cyber-security risks have become increasingly prominent. We propose an Intrusion Detection System (IDS) utilizing the DistilBERT model, which efficiently recognizes complex network behavior patterns. This system translates network traffic into sequences for enhanced contextual analysis, improving detection accuracy. DistilBERT's reduced encoding layers enhance the IDS model's performance. Our framework demonstrates effectiveness on the Car-Hacking and CICIDS2018 datasets, achieving an F1 score of 100% on the Car-Hacking dataset and at least a 1.28% improvement over other models on the CICIDS2018 dataset.