The Internet of Vehicles plays a crucial role in advancing intelligent transportation systems, with In-Vehicle Ethernet serving as the fundamental backbone network of the new generation of in-vehicle communication. However, In-Vehicle Ethernet faces various network security threats, including data theft, data tampering, and malicious attacks. This study focuses on network intrusion and security issues in In-Vehicle Ethernet, by analyzing the data characteristics of Audio Video Transport Protocol and potential network attack means. We innovatively propose a network intrusion detection method based on a weighted histogram algorithm. This method aims to enhance the security of In-Vehicle Ethernet. Experimental results show that the anomaly detection rate of the proposed weighted histogram algorithm in this study is 99.7%, which shows an improvement of 15.8% compared with the traditional Bayesian algorithm, and 6.9% higher than the decision tree algorithm. Thus, our approach enhances the stability and anti-attack ability of In-Vehicle Ethernet, providing a solid network security for In-Vehicle Networks.
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