CVMIDS: Cloud-Vehicle Collaborative Intrusion Detection System for Internet of Vehicles

As the evolution of 3GPP specification and the deployment of 5G network, Internet of Vehicles (IoVs) boom fireworks. However, its attack surface is expanded with the increased fusion of various functional interfaces, leading to easier penetration of vehicles. To deal with endless vehicle attacks, scholars propose many methods, where intrusion detection system (IDS) is an important branch. However, many IDSs are based on characteristics of single or specific types of vehicles, which limits model transplantation. Besides, 1-D features are usually utilized in existing IDSs, such as time, traffic, or voltage, etc., limiting the ability to detect attacks related to other dimensions. What is more, many IDSs harness machine learning algorithms and are deployed in vehicles simultaneously, which aggravates the computational burden. Therefore, we devise a cloud-vehicle collaborative IDS based on multidimensional features (CVMIDS) for IoV, called CVMIDS. It solves the problem of data heterogeneity by abstracting different vehicle data to the same feature space. Thus, data sets from different vehicles can be fed into one model for multiclassification, which naturally solves the problem of model transplantation. The feature space is established by combining features in dimensions of time, traffic, and voltage, thereby extending the types of attacks that CVMIDS can detect. Due to the deviated location of abnormal data in feature space compared with normal data, CVMIDS will misclassify vehicle data. Hence, CVMIDS can detect intrusions based on multiclassifying vehicles. Extensive experiments are conducted on three vehicles with different brands and numerical results corroborate the robustness and efficiency of CVMIDS.