Recent advances in the Internet of Things (IoT) and the adoption of IoT in vehicular networks have led to a new and promising paradigm called the Internet of Vehicles (IoV). However, the mode of communication in IoV being wireless in nature poses serious cybersecurity challenges. With many vehicles being connected in the IoV network, the vehicular data is set to explode. Traditional intrusion detection techniques may not be suitable in these scenarios with an extremely large amount of vehicular data being generated at an unprecedented rate and with various types of cybersecurity attacks being launched. Thus, there is a need for the development of advanced intrusion detection techniques capable of handling possible cyberattacks in these networks. Toward this end, we present an artificial intelligence (AI)-based intrusion detection architecture comprising Deep Learning Engines (DLEs) for identification and classification of the vehicular traffic in the IoV networks into potential cyberattack types. Also, taking into consideration the mobility of the vehicles and the realtime requirements of the IoV networks, these DLEs will be deployed on Multi-access Edge Computing (MEC) servers instead of running on the remote cloud. Extensive experimental results using popular evaluation metrics and average prediction time on a MEC testbed demonstrate the effectiveness of the proposed scheme.

中文翻译：

---

基于人工智能 (AI) 的车联网入侵检测架构

物联网 (IoT) 的最新进展以及物联网在车载网络中的采用催生了一种新的、有前途的范式，称为车联网 (IoV)。然而，IoV 中的通信模式本质上是无线的，这带来了严重的网络安全挑战。由于车联网网络中连接了许多车辆，因此车辆数据将呈爆炸式增长。传统的入侵检测技术可能不适合这些场景，以前所未有的速度生成大量车辆数据，并发起各种类型的网络安全攻击。因此，需要开发能够处理这些网络中可能的网络攻击的高级入侵检测技术。为此，我们提出了一种基于人工智能 (AI) 的入侵检测架构，包括深度学习引擎 (DLE)，用于将车联网网络中的车辆流量识别和分类为潜在的网络攻击类型。此外，考虑到车辆的移动性和车联网网络的实时性要求，这些 DLE 将部署在多访问边缘计算 (MEC) 服务器上，而不是在远程云上运行。在 MEC 测试平台上使用流行的评估指标和平均预测时间的广泛实验结果证明了所提出方案的有效性。考虑到车辆的移动性和车联网网络的实时性要求，这些 DLE 将部署在多访问边缘计算 (MEC) 服务器上，而不是运行在远程云上。在 MEC 测试平台上使用流行的评估指标和平均预测时间的广泛实验结果证明了所提出方案的有效性。考虑到车辆的移动性和车联网网络的实时性要求，这些 DLE 将部署在多访问边缘计算 (MEC) 服务器上，而不是运行在远程云上。在 MEC 测试平台上使用流行的评估指标和平均预测时间的广泛实验结果证明了所提出方案的有效性。