Multiscale residual temporal convolutional networks-based intrusion detection model in Internet of things

LIU Liwei; ZHAO Hongchao; LI Xuewei; SUN Bin

doi:10.11959/j.issn.1000-0801.2025046

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Multiscale residual temporal convolutional networks-based intrusion detection model in Internet of things

Research and Development | 更新时间：2025-10-31

- Multiscale residual temporal convolutional networks-based intrusion detection model in Internet of things
- Telecommunications Science Vol. 41, Issue 4, Pages: 164-175(2025)
- 作者机构：
  
  1.郑州工业应用技术学院大数据信息管理中心，河南郑州 451150
  2.周口职业技术学院信息工程学院，河南周口466002
- 作者简介：
- 基金信息：
  
  The Key Scientific and Technological Research Project of Henan Provincial Department of Science and Technology(232102210200);The Key Scientific Research Project of Henan Provincial Colleges and Universities(23B520036)
- DOI：10.11959/j.issn.1000-0801.2025046
  CLC： TN929.5
- Received：10 November 2024，
  
  Revised：2025-02-14，
  
  Published：20 April 2025
- 稿件说明：
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刘丽伟,赵红超,李学威等.基于多尺度残差时间卷积网络的物联网入侵检测模型[J].电信科学,2025,41(04):164-175.

LIU Liwei,ZHAO Hongchao,LI Xuewei,et al.Multiscale residual temporal convolutional networks-based intrusion detection model in Internet of things[J].Telecommunications Science,2025,41(04):164-175.
刘丽伟,赵红超,李学威等.基于多尺度残差时间卷积网络的物联网入侵检测模型[J].电信科学,2025,41(04):164-175. DOI： 10.11959/j.issn.1000-0801.2025046.

LIU Liwei,ZHAO Hongchao,LI Xuewei,et al.Multiscale residual temporal convolutional networks-based intrusion detection model in Internet of things[J].Telecommunications Science,2025,41(04):164-175. DOI： 10.11959/j.issn.1000-0801.2025046.

摘要

入侵检测可主动鉴别物联网流量攻击，它是维护物联网安全的重要措施。为此，提出基于多尺度残差时间卷积网络的入侵检测模型（multiscale residual temporal convolutional networks-based intrusion detection model，MRID）。MRID采用多尺度残差时间卷积模块，以增强网络学习时空的表征能力。同时，MRID采用了一个改进的流量注意力机制，帮助模型在学习过程中更关注重要特征。MRID可便捷应用于基于雾层的物联网架构中，以提供高效的实时入侵检测。利用数据集CICIDS2017和CSE-CIC-IDS2018验证MRID的性能。性能分析表明，MRID提高了入侵检测的效率，并在保持计算效率的同时，增强了模型的鲁棒性。

Abstract

Intrusion detection can actively identify Internet of things (IoT) traffic attacks

which is an important measure to maintain IoT security. Therefore

multiscale residual temporal convolutional networks-based intrusion detection model (MRID) was proposed. In MRID

a multiscale residual temporal convolutional module was utilized to enhance the network capability in learning spatiotemporal representations. An improved traffic attention mechanism was introduced to estimate the importance score that helps the model to concentrate on important information during leaning. The proposed MRID was easily integrated into a fog-enabled IoT to offer efficient real-time intrusion detection. Finally

empirical evaluations on two recent datasets (CICIDS2017 and CSE-CIC-IDS2018) were conducted

demonstrating that MRID improved the efficiency of intrusion detection and increased the robustness of model while maintaining computational efficiency.

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references

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