Abstract: With the increasing complexity of in-vehicle networks and the diversity of vehicle-to- everything (V2X) connections, the cybersecurity risks faced by the internet of vehicles (IoV) have significantly escalated. Addressing the issues of insufficient feature extraction and inaccurate model classification in existing intrusion detection systems, a novel intrusion detection model for IoV based on feature stacking and ensemble learning is proposed. This model slices one-dimensional data traffic into segments based on feature steps, stacks them into images in the third dimension, and utilizes the VGG19 model to extract specific types of features, the Xception model to capture intra-channel and inter-channel information, and the Inception model to process complex image categories and obtain multi-scale information. These three models are then integrated into the CS-IDS model. The proposed model was tested on two open-source IoV datasets, Car-Hacking and the traffic dataset CIC-IDS2017, achieving F1 scores of 99.97% and 96.44%, respectively. Moreover, the model can complete rapid detection of a single traffic flow within 12 ms, demonstrating the effectiveness and availability of the proposed CS-IDS model.

Key words: intrusion detection, ensemble learning, feature stacking, internet of vehicles(IoV) ,