An Internet of Vehicles Intrusion Detection System Based on a Convolutional Neural Network

Abstract

With the continuous development of the Internet of Vehicles, vehicles are no longer isolated nodes, but become a node in the car network. The open Internet will introduce traditional security issues into the Internet of Things. In order to ensure the safety of the networked cars, we hope to set up an intrusion detection system (IDS) on the vehicle terminal to detect and intercept network attacks. In our work, we designed an intrusion detection system for the Internet of Vehicles based on a convolutional neural network, which can run in a low-powered embedded vehicle terminal to monitor the data in the car network in real time. Moreover, for the case of packet encryption in some car networks, we have also designed a separate version for intrusion detection by analyzing the packet header. Experiments have shown that our system can guarantee high accuracy detection at low latency for attack traffic.