基于动态类权重的卷积神经网络攻击检测模型

樊荣

doi:10.11959/j.issn.1000-0801.2025113

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基于动态类权重的卷积神经网络攻击检测模型

研究与开发 | 更新时间：2025-09-04

- 基于动态类权重的卷积神经网络攻击检测模型
- Dynamical class-weighted-based convolutional neural networks attack detection model
- 电信科学 2025年41卷第8期页码：176-185
- 作者机构：
  
  驻马店职业技术学院，河南驻马店 463000
- 作者简介：
  
  [ "樊荣（1994- ），男，驻马店职业技术学院科研与信息化处讲师，主要研究方向为计算机应用技术。" ]
- 基金信息：
  
  2021年度河南省高等学校重点科研项目(21B880052)
- DOI：10.11959/j.issn.1000-0801.2025113
  中图分类号： TN929.5
- 收稿：2024-11-06，
  
  修回：2025-03-21，
  
  纸质出版：2025-08-20
- 稿件说明：
移动端阅览
樊荣.基于动态类权重的卷积神经网络攻击检测模型[J].电信科学,2025,41(08):176-185.

FAN Rong.Dynamical class-weighted-based convolutional neural networks attack detection model[J].Telecommunications Science,2025,41(08):176-185.
樊荣.基于动态类权重的卷积神经网络攻击检测模型[J].电信科学,2025,41(08):176-185. DOI： 10.11959/j.issn.1000-0801.2025113.

FAN Rong.Dynamical class-weighted-based convolutional neural networks attack detection model[J].Telecommunications Science,2025,41(08):176-185. DOI： 10.11959/j.issn.1000-0801.2025113.

摘要

入侵检测系统（intrusion detection system，IDS）作为物联网安全防御的核心组件，其性能直接影响网络的安全性。然而，入侵检测数据集中类样本的不平衡分布降低了入侵检测系统对少数类样本的检测性能。为解决这一问题，提出一种基于动态类权重的卷积神经网络的入侵检测（dynamical class-weighted-based convolutional neural network intrusion detection，DCID）模型。DCID模型采用一维卷积神经网络（1-D CNN）结构，并引入基于动态类权重的损失函数，使得DCID模型不仅能保持对多数类样本的高检测性能，也能显著提升对少数类样本的检测能力。为验证DCID模型的有效性，使用数据集CICIDS 2017进行实验。实验结果表明，与典型的机器学习模型相比，DCID模型在精确率、召回率和F1值方面表现出明显的优势。此外，还对比了不同损失函数下DCID模型的检测性能，结果表明基于动态类权重的损失函数能够有效提升少数类样本的检测性能。

Abstract

The intrusion detection system (IDS) as a core component of IoT security defense

was directly impacted in its performance

which in turn affected the overall security of the network. However

the imbalanced distribution of class samples in intrusion detection datasets is found to reduce the detection performance of IDS for minority class samples. To address this issue

a dynamical class-weighted-based convolutional neural network intrusion detection (DCID) model was proposed. The DCID model utilized a one-dimensional convolutional neural network (1-D CNN) structure and introduced a dynamical class-weighted loss function

enabling the DCID model to not only maintain high detection performance for majority class samples but also significantly enhance the detection capability for minority class samples. To validate the effectiveness of the DCID model

experiments were conducted using the CICIDS 2017 dataset. The experimental results demonstrate that

compared to typical machine learning models

the DCID model exhibites significant advantages in terms of precision

recall

and F1-score. Additionally

the detection performance of the DCID model under different loss functions was compared

and the results indicated that the dynamical class-weighted loss function effectively improved the detection performance for minority class samples.

关键词

Keywords

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