Vehicular-Network-Intrusion Detection Based on a Mosaic-Coded Convolutional Neural Network

Rong Hu, Zhongying Wu, Yong Xu and Taotao Lai
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Rong Hu: Fujian Provincial Key Laboratory of Big Data Mining and Application, Fujian University of Technology, Fuzhou 350118, China
Zhongying Wu: Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China
Yong Xu: Fujian Provincial Key Laboratory of Big Data Mining and Application, Fujian University of Technology, Fuzhou 350118, China
Taotao Lai: Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350108, China

Mathematics, 2022, vol. 10, issue 12, 1-21

Abstract: With the development of Internet of Vehicles (IoV) technology, the car is no longer a closed individual. It exchanges information with an external network, communicating through the vehicle-mounted network (VMN), which, inevitably, gives rise to security problems. Attackers can intrude on the VMN, using a wireless network or vehicle-mounted interface devices. To prevent such attacks, various intrusion-detection methods have been proposed, including convolutional neural network (CNN) ones. However, the existing CNN method was not able to best use the CNN’s capability, of extracting two-dimensional graph-like data, and, at the same time, to reflect the time connections among the sequential data. Therefore, this paper proposed a novel CNN model, based on two-dimensional Mosaic pattern coding, for anomaly detection. It can not only make full use of the ability of a CNN to extract grid data but also maintain the sequential time relationship of it. Simulations showed that this method could, effectively, distinguish attacks from the normal information on the vehicular network, improve the reliability of the system’s discrimination, and, at the same time, meet the real-time requirement of detection.

Keywords: Internet of Vehicles; Control Area Network Bus; intrusion detection; intelligent connected vehicle; convolutional neural network; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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