论文标题
转移学习和优化的CNN的基于CNN的入侵检测系统
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles
论文作者
论文摘要
现代车辆，包括自动驾驶汽车和互联车辆，越来越多地与外部世界联系起来，这可以实现各种功能和服务。但是，改善的连通性还增加了车辆互联网（IOV）的攻击表面，从而导致其脆弱性对网络威胁。由于车辆网络中缺乏身份验证和加密程序，入侵检测系统（IDSS）是保护现代车辆系统免受网络攻击的重要方法。在本文中，使用卷积神经网络（CNN）和高参数优化技术为IOV系统提出了转移学习和集合学习的ID。在实验中，提议的IDS在两个众所周知的公共基准IOV安全数据集上证明了99.25％的检测率和F1分数：汽车黑客数据集和CICIDS2017数据集。这显示了所提出的ID在车内和外部车辆网络中均具有网络攻击检测的有效性。
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.
