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  5. High-Efficiency Encryption and Authentication Network Security for Automotive Ethernet

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Article
English
2022

High-Efficiency Encryption and Authentication Network Security for Automotive Ethernet

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0 Files

English
2022
International Journal of Modeling and Optimization
DOI: 10.7763/ijmo.2022.v12.797

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Yujing Wu
Yujing Wu

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LI Jia-ming
Shuo Fu
Yujing Wu
+1 more

Abstract

Automotive ethernet has the advantages of high bandwidth, low latency, and strong compatibility, which meets the needs of new energy vehicles for the development of network integration. Automotive ethernet can not only solve the problem of increased wiring harness and complicated wiring in the intelligentization of automotive electronics, but also can improve the comfort, reliability and multiple safety of the car. Although the car is connected to the smart phone, Bluetooth, Internet and other network systems to improve the driving pleasure for the driver, but it brings hacker attacks, security loopholes and other car network security problems that cannot be ignored, which seriously affects the safe driving of the car, personal privacy, and even endanger public safety. In this paper, we focus on the need for the network security of automotive ethernet, and analyses problem in encryption authentication algorithm. An improved AES-128 encryption algorithm and an improved MD5 authentication algorithm are proposed innovatively. Through the experimental simulation of CANoe.Ethernet, the improved AES-128 encryption algorithm proposed in this paper is 15% more efficient than the traditional encryption algorithm, and the improved MD5 authentication algorithm is 4 times faster than the traditional authentication algorithm. Thus, the active network security performance of automotive ethernet is further improved.

How to cite this publication

LI Jia-ming, Shuo Fu, Yujing Wu, Yinan Xu (2022). High-Efficiency Encryption and Authentication Network Security for Automotive Ethernet. International Journal of Modeling and Optimization, pp. 36-43, DOI: 10.7763/ijmo.2022.v12.797.

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Publication Details

Type

Article

Year

2022

Authors

4

Datasets

0

Total Files

0

Language

English

Journal

International Journal of Modeling and Optimization

DOI

10.7763/ijmo.2022.v12.797

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