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An Intrusion Detection Method for Internet of Vehicles Based on Improved WGAN-GP and ResNet
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[1]WEI Mingjun,LI Feng,LIU Yazhi,et al.An Intrusion Detection Method for Internet of Vehicles Based on Improved WGAN-GP and ResNet[J].Journal of Zhengzhou University (Engineering Science),2024,45(04):30-37.[doi:10.13705/ j.issn.1671-6833.2024.04.008]
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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 04 Page number: 30-37 Column: Public date: 2024-06-16
Title:
An Intrusion Detection Method for Internet of Vehicles Based on Improved WGAN-GP and ResNet
Author(s):
WEI Mingjun1; 2;  LI Feng1;  LIU Yazhi1; 2;  LI Hui1
1.College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China; 2.Hebei Provincial Key Laboratory of Industrial Intelligent Perception, Tangshan 063210, China
Keywords:
Internet of Vehicles;  intrusion detection;  generate adversarial networks;  residual neural network;  fea ture fusion
CLC:
TP393 TN929.5
DOI:
10.13705/ j.issn.1671-6833.2024.04.008
Abstract:
In order to protect the Internet of Vehicles system from the threat of network attacks and improve the ac curacy of intrusion detection, a new intrusion detection method (AQVAE-RGSNet) was proposed for the character istics of large data flow and unbalanced attack types in the vehicle network. Firstly, the adversarial quantized varia tional auto encoder was used to process the vehicle network data imbalance. And it was constructed by combining the vector quantized variational auto encoder-2 and the generative adversarial network with gradient penalty to alle viate the extremely unbalanced number of samples of abnormal attack types in the dataset. Afterwards, the ResNet and improved segmented residual neural network were used to learn the input sample data and predict its attack cat egory. The experimental results indicated that AQVAE-RGSNet achieved F1 scores of 0.998 6 and 0.999 7 on the vehicle networking dataset CICDS2017 and CAN-intrusion-dataset, respectively. On the premise of ensuring the best training effect, it could identify attack threats more effectively in the vehicle network.
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