Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks for their everyday functions on the road so that safety of passengers and vehicles can be ensured. This article presents a holistic review of cybersecurity attacks on sensors and threats regarding multi-modal sensor fusion. A comprehensive review of cyberattacks on intra-vehicle and inter-vehicle communications is presented afterward. Besides the analysis of conventional cybersecurity threats and countermeasures for CAV systems, a detailed review of modern machine learning, federated learning, and blockchain approach is also conducted to safeguard CAVs. Machine learning and data mining-aided intrusion detection systems and other countermeasures dealing with these challenges are elaborated at the end of the related section. In the last section, research challenges and future directions are identified.

中文翻译：

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用于联网车辆网络安全的机器学习和区块链技术

未来的联网自动驾驶汽车 (CAV) 的日常道路功能必须免受网络攻击，从而确保乘客和车辆的安全。本文对传感器的网络安全攻击和多模式传感器融合的威胁进行了全面回顾。随后对车辆内和车辆间通信的网络攻击进行了全面回顾。除了分析 CAV 系统的传统网络安全威胁和对策外，还对现代机器学习、联邦学习和区块链方法进行了详细回顾，以保护 CAV。机器学习和数据挖掘辅助的入侵检测系统以及应对这些挑战的其他对策在相关部分的末尾进行了详细阐述。在最后一节中，