[1]魏明军,李 凤,刘亚志,等.基于改进WGAN-GP和ResNet的车联网入侵检测方法[J].郑州大学学报(工学版),2024,45(04):30-37.[doi:10.13705/ j.issn.1671-6833.2024.04.008]
　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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基于改进WGAN-GP和ResNet的车联网入侵检测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年04期
页码:
30-37
栏目:
出版日期:
2024-06-16

文章信息/Info

Title:
An Intrusion Detection Method for Internet of Vehicles Based on Improved WGAN-GP and ResNet
文章编号:
1671-6833(2024)04-0030-08
作者:
魏明军1; 2;  李 凤1;  刘亚志1; 2;  李 辉1
1.华北理工大学 人工智能学院,河北 唐山 063210;2.河北省工业智能感知重点实验室,河北 唐山 063210
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
关键词:
车联网;  入侵检测;  生成对抗网络;  残差神经网络;  特征融合
Keywords:
Internet of Vehicles;  intrusion detection;  generate adversarial networks;  residual neural network;  fea ture fusion
分类号:
TP393 TN929.5
DOI:
10.13705/ j.issn.1671-6833.2024.04.008
文献标志码:
A
摘要:
为保护车联网系统免受网络攻击的威胁,同时提高车联网入侵检测的准确率,针对车辆网络数据流量大 且攻击类型不平衡的特点,提出了一种新的车联网入侵检测方法(AQVAE-RGSNet)。该方法通过一种对抗量化变 分自编码器以对车辆网络数据进行不平衡处理,该编码器通过结合矢量量化变分自编码器与带梯度惩罚的生成对 抗网络进行构建,以缓解数据集中异常攻击类型样本数量极度不平衡的问题,并使用ResNet网络与改进的分段残 差神经网络对输入的样本数据进行联合学习并预测其攻击类型。实验结果表明:AQVAE-RGSNet在车联网数据集 CICIDS2017和CAN-intrusion-dataset上的F1得分分别达到了0.998 6和0.999 7;在保证最佳训练效果的前提下, 能够更有效地识别车辆网络之中的攻击威胁。
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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更新日期/Last Update: 2024-06-14