An intrusion detection model for vehicular networks based on optimized feature stacking and ensemb
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 sig-nificantly escalated.Addressing the issues of insufficient feature extraction and inaccurate model classifi-cation 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 informa-tion,and the Inception model to process complex image categories and obtain multi-scale information.Th ese 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.
intrusion detectionensemble learningfeature stackinginternet of vehicles(IoV)
万方数据
维普
