移动学习和优化有线电视新闻网的车辆互联网入侵探测系统 (A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles)

from arxiv, Accepted and to appear in IEEE International Conference on Communications (ICC); Code is available at Github link: https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning

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.

翻译：现代车辆,包括自主车辆和相联车辆,越来越多地与外部世界连接,从而能够提供各种功能和服务;然而,连通性的改善也增加了车辆互联网的攻击面,使其易受网络威胁;由于缺乏车辆网络的认证和加密程序,入侵探测系统(IDS)是保护现代车辆系统免遭网络攻击的重要办法;本文件提议,利用神经网络和超参数优化技术,为IoV系统提供传输学习和共同学习的IDS;在实验中,拟议的IoV系统在两个众所周知的公共基准IoV安全数据集上展示了99.25%以上的探测率和F1分数:汽车载重数据组和CICIDS-2017数据集;这表明了拟议用于在车内和外部车辆网络进行网络攻击检测的IDS的有效性。