Title: Design of automatic detection system for vehicle networking communication abnormal data based on CAN bus

Authors: Qun Le; Kun Jiang; Feng Zhang

Addresses: College of Electronic Engineering, Anhui Wenda Institute of Information Engineering, Anhui Hefei, 230022, China ' Electric Engineering Section, Department of Mechanical Engineering, Army Academy of Artillery and Air Defense, Hefei, 230031, China ' Electric Engineering Section, Department of Mechanical Engineering, Army Academy of Artillery and Air Defense, Hefei, 230031, China

Abstract: Aiming at the problems of low detection rate and accuracy rate, high false detection rate and missed detection rate caused by the proportion of abnormal data in the current design system when detecting abnormal data in the process of vehicle networking communication, an automatic detection system of abnormal data in vehicle networking communication based on CAN bus is proposed and designed. The system mainly includes two parts: vehicle networking communication subsystem and vehicle networking communication data anomaly detection subsystem. The vehicle networking communication subsystem includes vehicle networking communication data acquisition unit and C/S structure. The real-time interaction process between client and server is given. The experimental results and discussions prove that the design system can automatically detect abnormal data in vehicle networking communication, and it is less affected by the proportion of abnormal data, which has the advantages of higher detection rate and accuracy, lower false detection rate and missed detection rate. At the same time, the preliminary conclusion is drawn that the sampling ratio will hardly affect the detection results.

Keywords: CAN bus; vehicle networking; communication; abnormal data; automatic detection; system design.

DOI: 10.1504/IJICT.2020.105607

International Journal of Information and Communication Technology, 2020 Vol.16 No.2, pp.123 - 139

Received: 29 Dec 2018
Accepted: 25 Feb 2019
Published online: 06 Mar 2020 *

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