Neural network based partial tomography for in-vehicle network monitoring

Ibraheem, Amani, Sheng, Zhengguo, Parisis, George and Tian, Daxin (2021) Neural network based partial tomography for in-vehicle network monitoring. IEEE International Communications Conference (ICC), Online, 14-23 June 2021. Published in: 2021 IEEE International Conference on Communications Workshops (ICC Workshops). 1-6. IEEE, Montreal, QC, Canada. ISSN 2164-7038 ISBN 9781728194424

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In-vehicle network monitoring is one of the important elements in vehicular network management and security. Most of the existing network monitoring approaches rely on measuring every part of the network. Such approaches overburden the network by transmitting active probes. In this work, we propose a new in-vehicle network monitoring approach that benefits from network tomography and the advances in deep learning to infer the network delay performance. Specifically, the available measurements can be used to estimate the performance of the remaining network where direct measurements cannot be applied. Performance evaluation has been conducted using in-vehicle network simulation with different TSN (Time-Sensitive Network) traffics and the proposed monitoring approach shows the delay estimation accuracy of up to 99%.

Item Type: Conference Proceedings
Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Keywords: in-vehicle networks, network tomography, Neural Network
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 08 Apr 2021 09:36
Last Modified: 04 Mar 2022 17:18

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