A Many-Objective Optimization Based Intelligent Intrusion Detection Algorithm for Enhancing Security of Vehicular Networks in 6G

With accelerated ensemble of the Internet of Things technology and automotive industry, vehicular network has been established as powerful tools. However, it is a significant challenge for dynamic and heterogeneous vehicular network to meet high requirements of the sixth-generation (6G) network such...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 70; no. 6; pp. 5234 - 5243
Main Authors: Zhang, Zhixia, Cao, Yang, Cui, Zhihua, Zhang, Wensheng, Chen, Jinjun
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
6G mobile communication
Algorithms
Automobile industry
Controller area network
Controller area network (CAN)
Data models
Encryption
Internet of Things
Intrusion detection
Intrusion detection systems
Machine learning
machine learning (ML)
many-objective optimization
Multiple objective analysis
Network reliability
Optimization
Protocols
Security
Vehicles
vehicular networks security
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:With accelerated ensemble of the Internet of Things technology and automotive industry, vehicular network has been established as powerful tools. However, it is a significant challenge for dynamic and heterogeneous vehicular network to meet high requirements of the sixth-generation (6G) network such as high reliability and high security. To address this challenge, we design a novel weight-based ensemble machine learning algorithm (WBELA) to identify abnormal messages of vehicular Controller Area Network (CAN) bus network. Then, we establish a model based on many-objective optimization for intrusion detection of CAN bus network. To support this model, a many-objective optimization algorithm based on balance convergence and diversity (MaOEA-BCD) is designed. Open-source CAN bus message data sets and tamper attack scenarios are used to evaluate the effectiveness of proposed algorithm for different ID data frames. Experimental results revealed that proposed methods significantly enhance precision, reduce the false positive rate and have better performance than other methods so as to enhance security of vehicular networks in 6G.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3057074