As VANETs have been widely applied in various fields including entertainment and safety- related applications like autonomous driving, malicious intrusions into VANETs may lead to disastrous results. Hence, intrusion detection accuracy as well as efficiency is sensitive to the normal operation of VANETs. Regarding this, in this article we propose an architecture of IDPS for VANETs. One of the highlights of the architecture is that it applies RL throughout the architecture in order to deal with the dynamics of VANETs and to make proper decisions according to current VANETs states, aiming at high detection accuracy. On the other hand, the architecture is deployed in EC in an attempt to obtain low detection latency with high processing efficiency, since VANETs IDPS is sensitive to latency, especially for safety applications. A case study is conducted to assess the validity of the proposed VANETs IDPS in EC, with the results revealing that it holds the capacity to detect and prevent intrusion in VANETs in complex environments.

中文翻译：

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边缘计算中用于车载网络的强化学习授权IDPS

由于VANET已广泛应用于包括娱乐和安全相关应用（如自动驾驶）在内的各个领域，恶意入侵VANET可能会导致灾难性的后果。因此，入侵检测的准确性和效率对VANET的正常运行很敏感。对此，在本文中，我们提出了用于VANET的IDPS体系结构。该体系结构的一大亮点是，它在整个体系结构中都采用了RL，以应对VANET的动态变化并根据当前VANET的状态做出适当的决策，以实现更高的检测精度。另一方面，由于VANET IDPS对延迟敏感，特别是对于安全应用程序，该架构已部署在EC中，以期获得具有较高处理效率的低检测延迟。