面向车联网的基于卷积神经网络的入侵检测模型

张锐

doi:10.11959/j.issn.1000-0801.2024243

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面向车联网的基于卷积神经网络的入侵检测模型

专题：智能网联汽车 | 更新时间：2024-12-30

- 面向车联网的基于卷积神经网络的入侵检测模型
- An intrusion detection model based on convolution neural network for Internet of vehicles
- 电信科学 2024年40卷第12期页码：51-62
- 作者机构：
  
  驻马店职业技术学院信息工程学院，河南驻马店 463003
- 作者简介：
  
  [ "张锐（1980- ），女，驻马店职业技术学院信息工程学院副教授，主要研究方向为计算机多媒体技术。" ]
- 基金信息：
  
  河南省科技攻关项目(212102210515)
- DOI：10.11959/j.issn.1000-0801.2024243
  中图分类号： TP393
- 收稿日期：2024-07-06，
  
  修回日期：2024-11-18，
  
  纸质出版日期：2024-12-20
- 稿件说明：
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张锐.面向车联网的基于卷积神经网络的入侵检测模型[J].电信科学,2024,40(12):51-62.

ZHANG Rui.An intrusion detection model based on convolution neural network for Internet of vehicles[J].Telecommunications Science,2024,40(12):51-62.
张锐.面向车联网的基于卷积神经网络的入侵检测模型[J].电信科学,2024,40(12):51-62. DOI： 10.11959/j.issn.1000-0801.2024243.

ZHANG Rui.An intrusion detection model based on convolution neural network for Internet of vehicles[J].Telecommunications Science,2024,40(12):51-62. DOI： 10.11959/j.issn.1000-0801.2024243.

摘要

为了提高车联网入侵检测的准确率，提出了基于超参数优化卷积神经网络的集成的入侵检测系统（hyper-parameter optimization convolution neural network-based ensemble Intrusion detection system，CNES）模型。CNES模型利用卷积神经网络构建集成学习的基学习器，并利用粒子群优化算法优化卷积神经网络的超参数，进而优化卷积神经网络模型。利用平均法和级联法的集成策略构建集成学习模型，提高检测攻击的准确率。通过车内网络数据集Car-Hacking和车外网络数据集CICIDS2017验证CNES模型的性能。性能分析表明，提出的CNES模型有效地提高了检测网络攻击的性能。在Car-Hacking数据集上，CNES模型的F1值达到100%。

Abstract

In order to improve the accuracy of detecting the cyber-attacks in Internet of vehicles

hyper-parameter optimization convolution neural network-based ensemble Intrusion detection system (CNES) was proposed. In CNES

the convolution neural network (CNN) was adopted to serve as based learner in ensemble learning. Moreover

the particle swarm optimization was utilized to optimize the hyber-parameters of the CNN

and then CNN model was optimized. Confidence averaging and concatenation techniques were constructed to improve the accuracy. The performance of the proposed CNES was measured based on Car-Hacking and CICIDS2017 datasets. This shows the effectiveness of the proposed CNES for cyber-attack detection. The CNES achieves F1 score of 100% on Car-Hacking dataset.

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references

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