The arrival of the 5G era brings unprecedented opportunities and challenges to vehicular networks. As one of the most innovative and important technologies of 5G, network slicing can separate the physical substrate network into multiple logical networks, and meet the requirements of various vehicle services simultaneously. Therefore, its superiority in vehicular networks has attracted extensive attention. However, with the gradual transition of 5G and beyond vehicular networking (5G/B5G-VN) from theory to practice, little attention has been paid to the security of network slicing. Without security guarantee, it is difficult for network slicing to be formally put into use and widely accepted. In view of this, we propose a tutorial on the network slicing security for 5G/B5G-VN, aiming to present the potential of network slicing for security protection and also analyze its security threats from multiple perspectives. In addition, a case study of using deep reinforcement learning for recovery after attacks is provided to improve the service success rate of slices and imbue 5G/B5G-VN with a certain degree of resilience.

中文翻译：

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5G 及其他车载网络中的安全可靠切片

5G时代的到来给车载网络带来了前所未有的机遇和挑战。网络切片作为5G最具创新性和重要的技术之一，可以将物理底层网络分割成多个逻辑网络，同时满足各种车载业务的需求。因此，其在车载网络中的优越性引起了广泛的关注。然而，随着 5G 及超车联网（5G/B5G-VN）从理论到实践的逐步过渡，网络切片的安全性却很少受到关注。没有安全保障，网络切片很难正式投入使用并被广泛接受。有鉴于此，我们提出5G/B5G-VN网络切片安全教程，旨在展示网络切片在安全防护方面的潜力，并从多个角度分析其安全威胁。此外，还提供了使用深度强化学习进行攻击后恢复的案例研究，以提高切片的服务成功率，并为 5G/B5G-VN 赋予一定的弹性。