A Cloud Collaborative-Based Intrusion Detection and Prevention System for IVN

With the increasing intelligence and convenience of modern vehicles, Internet-of-Vehicle (IoV) technology plays a pivotal role in driving these advancements. While IoV enhances user services, it also introduces security threats, particularly intrusions into the In-Vehicle Network (IVN). Attackers can exploit onboard external network interfaces, posing risks to vehicle safety and operation. Besides, deploying an Intrusion Detection System (IDS) on vehicles with limited computational resources exacerbates the computational burden. To address these challenges, this paper proposes a Cloud Collaborative-based Intrusion Detection and Prevention System (CC-IDPS). Firstly, this study analyzes existing in-vehicle intrusion detection datasets and, combined with real in-vehicle/simulation data, constructs the In-vehicle Attack Dataset (IVAD). Secondly, the CC-IDPS employs a sliding window approach for feature extraction from multiple Controller Area Network (CAN) IDs, and leverages Bidirectional Encoder Representation from Transformers (BERT) models for in-vehicle traffic classification. Based on classification results, the CC-IDPS applies treatments to in-vehicle traffic. Subsequently, the proposed Encode2ID algorithm encodes and stores malicious traffic in the database of cloud, facilitating subsequent model training. Experimental results demonstrate that, when applied to the IVAD dataset, the model exhibits lower detection accuracy and F1-score compared to other datasets. Notably, the CC-IDPS outperforms other machine learning and deep learning models in detecting performance on the IVAD dataset.