基于深度学习和SVM-RFE的网络入侵检测模型

叶青; 张延年; 吴昊

doi:10.11959/j.issn.1000-0801.2025100

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基于深度学习和SVM-RFE的网络入侵检测模型

研究与开发 | 更新时间：2025-08-07

- 基于深度学习和SVM-RFE的网络入侵检测模型
- Deep learning and support vector machine-recursive feature elimination-based network intrusion detection model
- 电信科学 2025年41卷第7期页码：108-119
- 作者机构：
  
  1.江苏省高港中等专业学校，江苏泰州 225324
  2.南京交通职业技术学院电子信息工程学院，江苏南京 211188
- 作者简介：
  
  [ "叶青（1973- ），男，江苏省高港中等专业学校讲师，主要研究方向为人工智能、物联网应用。" ]
  [ "张延年（1977- ），男，南京交通职业技术学院电子信息工程学院副教授，主要研究方向为大数据、人工智能。" ]
  [ "吴昊（1973- ），男，南京交通职业技术学院电子信息工程学院教授，主要研究方向为智能交通、计算机网络。" ]
- 基金信息：
  
  南京交通职业技术学院重大课题项目(JZ2304);南京交通职业技术学院重点课题项目(JZ2306)
- DOI：10.11959/j.issn.1000-0801.2025100
  中图分类号： TP393
- 收稿日期：2024-10-11，
  
  修回日期：2024-12-18，
  
  纸质出版日期：2025-07-20
- 稿件说明：
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叶青,张延年,吴昊.基于深度学习和SVM-RFE的网络入侵检测模型[J].电信科学,2025,41(07):108-119.

YE Qing,ZHANG Yannian,WU Hao.Deep learning and support vector machine-recursive feature elimination-based network intrusion detection model[J].Telecommunications Science,2025,41(07):108-119.
叶青,张延年,吴昊.基于深度学习和SVM-RFE的网络入侵检测模型[J].电信科学,2025,41(07):108-119. DOI： 10.11959/j.issn.1000-0801.2025100.

YE Qing,ZHANG Yannian,WU Hao.Deep learning and support vector machine-recursive feature elimination-based network intrusion detection model[J].Telecommunications Science,2025,41(07):108-119. DOI： 10.11959/j.issn.1000-0801.2025100.

摘要

网络入侵检测系统是对抗各种网络威胁的有效手段。然而，网络入侵数据中存在大量冗余信息和分布不平衡问题，为此，提出基于深度学习和支持向量机的递归特征消除算法的网络入侵检测（DLRF）模型。DLRF模型利用基于支持向量机的递归特征消除算法进行特征权重排序，选择重要特征。同时，结合过采样和欠采样技术解决数据样本分布不平衡的问题。利用3个深度学习算法构建集成框架的基学习器，并利用深度神经网络构建元学习器，进而提升DLRF模型检测网络攻击的性能。通过两个典型的网络入侵数据集UNSW-NB15和数据集CICIDS 2017验证DLRF模型的性能。性能分析表明，DLRF模型在这两个数据集上的准确率分别为0.906 8、0.996 8，F1值（F1-score）分别为0.906 8、0.996 0。

Abstract

Network intrusion detection system has gained attention as an effective means of combating various cyber threats. However

there are a lot of redundant information and unbalanced distribution problems in network intrusion data

therefore

deep learning and support vector machine-recursive feature elimination-based network intrusion detection model (DLRF) was proposed. The features were sorted by the support vector machine-recursive feature elimination algorithm and the important features were selected. Moreover

both oversampling and under-sampling techniques were utilized to tackle the unbalance problem of data sample distribution. Three deep learning algorithms were used to build the base learner of the ensemble framework

and deep neural network was used to build the meta-learner

so as to improve the performance of DLRF model to detect network attacks. The proposed framework was experimented with two publicly available and popular network traffic datasets

namely UNSW-NB15 and CICIDS-2017. The accuracy rates of the DLRF model on these two datasets are 0.906 8 and 0.996 8 respectively

and the F1-score are 0.906 8 and 0.996 0.

关键词

Keywords

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