Abstract
The rapid proliferation of smart vehicles, particularly connected vehicles, has led to a rise in cyberthreats. Ensuring the security of associated equipment has become a pressing concern. This article presents an analysis of various machine learning models for detecting message spoofing attacks on smart vehicles. These types of attacks can pose a significant risk to the safety and security of smart vehicles, with dangers such as accidents, hijacking incidents and other severe consequences. The findings indicate the potential of machine learning models in detecting message spoofing attacks. And the results underscore the need for robust security measures to prevent message spoofing attacks on smart vehicles.
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