Abstract—The in-vehicle bus network is an important part that directly affects the safety of the car, so the real-time, safety and reliability of the vehicle bus network must be guaranteed. Connected to smart phones, Bluetooth, Internet, etc., the car enhances the driving pleasure. On the other hand, it brings hacker attacks, security vulnerabilities and other security issues that cannot be ignored, which seriously affects the car's driving safety, personal privacy, and even threatens public safety. The characteristics of the vehicle bus information are hexadecimal data with consistency, time series, and ID. This paper diagnoses the abnormality in the vehicle bus, firstly preprocesses the bus data, and then uses the convolutional neural network method to detect the abnormality of the CAN bus information. By adjusting the neural network parameters during the experiment, the final detection rate is as high as 99.9%. It can well guarantee the security of bus data.
Index Terms—Vehicle bus, convolutional neural network, can, network security, detection rate.
The authors are with the College of Engineering of Yanbian University, Yanji, 133002, China (e-mail: ynxu@ybu.edu.cn).
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Cite:Shi-Nan Wang, Yu-Jing Wu, and Yi-Nan Xu*, "Research of CAN Bus Information Anomaly Detection Based on Convolutional Neural Network," International Journal of Computer Theory and Engineering vol. 13, no. 2, pp. 42-46, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
