Internet of vehicles intrusion detection method based on CFS-COA feature selection and spatio-temporal feature extraction

Abstract

Abstract With the rapid spread of the Internet of Vehicles (IoV) technology, vehicle network security is facing increasingly severe challenges. Intrusion detection technology has become a crucial tool for ensuring the information security of IoV. Since the traffic data of the IoV is large and has spatio-temporal characteristics, most previous studies are based on a single deep learning method to extract temporal or spatial features, which does not fully extract features of IoV data. To address the above issues, a spatio-temporal feature extraction model with feature selection is proposed. First, to solve the problem of long detection time with huge data traffic, a new feature selection method is proposed to screen the optimal feature subset by combining the correlation-based feature selection method with the crayfish optimization algorithm (CFS-COA). Second, the selected optimal features are used in a spatio-temporal feature extraction model that combines a Temporal Convolutional Network and a Bidirectional Gated Recurrent Unit (TCN-BiGRU) for classification. Finally, the performance of the model is evaluated using two types of datasets: the NSL-KDD and UNSW-NB15 datasets for external communications, and the Car-Hacking dataset for in-vehicle networks. The experimental results indicate that the proposed model demonstrates high classification performance and lightweight characteristics, achieving 100% accuracy on the Car-Hacking dataset.