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The automotive industry must implement some of the best cybersecurity practices to protect electronic systems, communication networks, control algorithms, software, users, and data from malicious attacks. An artificial intelligence (AI) & machine learning (ML) based automotive cybersecurity system could help identify potential vulnerabilities in electronic vehicles as they are part of the vehicular cyber physical systems (CPS). The primary reason that cybersecurity challenges in the automotive domain differ significantly from those in other sectors are threats to computer systems within vehicular CPS can cause direct harm to the drivers of the vehicles. There is a pressing need for better understanding of various attacks and defensive approaches for vehicular CPS to better protect them against any potential threats. This study investigates AI/ML attacks and defenses for vehicular CPS systems to extract a better comprehension of how cybersecurity affects computational components within the vehicular CPS, what the standards are & how they differ, the types of prominent attacks against these systems, and finally an overview of defensive approaches for these attacks. We provide a comprehensive overview of the attacks and defensive techniques/methodologies against vehicular cyberphysical systems.
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