Attack detection model based on stacking ensemble learning for Internet of vehicles

XU Huibin; FANG Long; ZHANG Sha

doi:10.11959/j.issn.1000-0801.2024257

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Attack detection model based on stacking ensemble learning for Internet of vehicles

Topic： | 更新时间：2024-12-30

- Attack detection model based on stacking ensemble learning for Internet of vehicles
- Telecommunications Science Vol. 40, Issue 12, Pages: 38-50(2024)
- 作者机构：
  
  湖州师范学院信息工程学院，浙江湖州 313000
- 作者简介：
- 基金信息：
  
  The Huzhou Natural Science Foundation(2024YZ08)
- DOI：10.11959/j.issn.1000-0801.2024257
  CLC： TP183
- Received：18 October 2024，
  
  Revised：08 November 2024，
  
  Published：20 December 2024
- 稿件说明：
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徐会彬,方龙,张莎.车联网中基于stacking集成学习的攻击检测模型[J].电信科学,2024,40(12):38-50.

XU Huibin,FANG Long,ZHANG Sha.Attack detection model based on stacking ensemble learning for Internet of vehicles[J].Telecommunications Science,2024,40(12):38-50.
徐会彬,方龙,张莎.车联网中基于stacking集成学习的攻击检测模型[J].电信科学,2024,40(12):38-50. DOI： 10.11959/j.issn.1000-0801.2024257.

XU Huibin,FANG Long,ZHANG Sha.Attack detection model based on stacking ensemble learning for Internet of vehicles[J].Telecommunications Science,2024,40(12):38-50. DOI： 10.11959/j.issn.1000-0801.2024257.

摘要

由于无线网络的开放性，车联网容易受到网络攻击，如拒绝服务、模糊和欺骗

攻击。为此，提出融合随机森林（random forest，RF）和梯度提升决策树（gradient boosting decision tree，GBDT）的堆叠（stacking）的入侵检测（RG-IDS）模型。首先，RG-IDS模型利用自适应合成采样（adaptive synthetic sampling，ADASYN）算法对不平衡类别的数据样本进行近邻采样，进而生成更多同类别的近似样本，形成相对平衡的样本数据。其次，RG-IDS模型利用GBDT评估特征的重要性，并选择具有重要特征的样本数据，建立轻量级分类器。最后，RG-IDS采用基于

折交叉验证的堆叠方法，降低过拟合的概率。将RF、GBDT和LightGBM分类器作为基学习器。采用数据集CICIDS 2017和NSL-KDD对RG-IDS模型进行实验测试。实验结果表明，RG-IDS模型可实现较高的F1值。

Abstract

Due to openness of wireless communication

Internet of vehicles (IoV) is vulnerable to many cyber-attacks such as denial of service

spoofing and fuzzy attacks. Therefore

random forest (RF) and gradient boosting decision tree-based stacking intrusion detection (RF-IDS) model was proposed. Firstly

the adaptive synthetic sampling (ADASYN) algorithm was adopted to generate more similar samples through the nearest neighbor sampling strategy in order to balance the training samples of different categories

and form a relatively symmetric dataset. Secondly

GBDT was used to evaluate the importance of features and select sample data with important features to build a lightweight classifier. Finally

the

-fold cross-validation stacking method was used to reduce the probability of overfitting. RF

GBDT and LightGBM classifiers serve were used as base-learner. The RG-IDS model was tested by CICIDS 2017 and NSL-KDD datasets. The experimental results demonstrate that RG-IDS model can achieve a higher F1-score.

关键词

Keywords

references

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