Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

本论文提出了一种基于转移学习和集合学习的基于卷积神经网络和超参数优化技术的IDS，用于IoV系统的网络安全攻击检测以保护现代车辆系统。在实验中，该IDS在两个公共基准IoV安全数据集上表现出超过99.25％的检测率和F1分数，表明了其对车内和外部汽车网络中的网络攻击检测的有效性。

一种基于转移学习和优化卷积神经网络的物联网车辆入侵检测系统