基于改进WGAN-GP和ResNet的车联网入侵检测方法OA北大核心CSTPCD

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.