Abstract
With accelerated ensemble of the Internet of Things technology and automotive industry, vehicular network has been established as powerful tools. However, it is a significant challenge for dynamic and heterogeneous vehicular network to meet high requirements of the sixth-generation (6G) network such as high reliability and high security. To address this challenge, we design a novel weight-based ensemble machine learning algorithm (WBELA) to identify abnormal messages of vehicular Controller Area Network (CAN) bus network. Then, we establish a model based on many-objective optimization for intrusion detection of CAN bus network. To support this model, a many-objective optimization algorithm based on balance convergence and diversity (MaOEA-BCD) is designed. Open-source CAN bus message data sets and tamper attack scenarios are used to evaluate the effectiveness of proposed algorithm for different ID data frames. Experimental results revealed that proposed methods significantly enhance precision, reduce the false positive rate and have better performance than other methods so as to enhance security of vehicular networks in 6G.
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