基于联邦强化学习的面向边缘网络的入侵检测方法研究

丁凯; 黄宜都; 陶铭; 谢仁平

doi:10.11959/j.issn.2096-3750.2024.00442

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基于联邦强化学习的面向边缘网络的入侵检测方法研究

理论与技术 | 更新时间：2025-01-20

- 基于联邦强化学习的面向边缘网络的入侵检测方法研究
- Research on intrusion detection method for edge networks based on federated reinforcement learning
- 物联网学报 2024年8卷第4期页码：140-155
- 作者机构：
  
  1.东莞理工学院计算机科学与技术学院，广东东莞 523808
  2.人工智能与数字经济广东省实验室（深圳），广东深圳 518107
- 作者简介：
  
  [ "丁凯（1985‒ ），男，博士，东莞理工学院计算机科学与技术学院副教授，主要研究方向为物联网、智慧城市、机器人技术和移动互联应用等。" ]
  [ "黄宜都（1998‒ ），男，东莞理工学院计算机科学与技术学院硕士生，主要研究方向为物联网工程、联邦学习和网络流量检测等。" ]
  [ "陶铭（1986‒ ），男，博士，东莞理工学院计算机科学与技术学院教授、副院长，主要研究方向为人工智能、边缘计算和云计算等。" ]
  [ "谢仁平（1989‒ ），男，博士，东莞理工学院计算机科学与技术学院特聘副研究员，主要研究方向为计算机视觉、图像处理、目标检测、图像分割、图像融合、图像拼接和桥梁检测机器人等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62001113);广东省基础与应用基础研究基金资助项目(2021A1515010656);广东省人工智能与数字经济实验室（深圳）开放研究基金资助项目(GML-KF-22-02);东莞市社会发展科技项目(20221800902472)
- DOI：10.11959/j.issn.2096-3750.2024.00442
  中图分类号： TP301.6
- 纸质出版日期：2024-12-10，
  
  收稿日期：2024-10-14，
  
  修回日期：2024-11-25，
- 稿件说明：
移动端阅览
丁凯, 黄宜都, 陶铭, 等. 基于联邦强化学习的面向边缘网络的入侵检测方法研究[J]. 物联网学报, 2024,8(4):140-155.

DING KAI, HUANG YIDU, TAO MING, et al. Research on intrusion detection method for edge networks based on federated reinforcement learning. [J]. Chinese journal on internet of things, 2024, 8(4): 140-155.
丁凯, 黄宜都, 陶铭, 等. 基于联邦强化学习的面向边缘网络的入侵检测方法研究[J]. 物联网学报, 2024,8(4):140-155. DOI： 10.11959/j.issn.2096-3750.2024.00442.

DING KAI, HUANG YIDU, TAO MING, et al. Research on intrusion detection method for edge networks based on federated reinforcement learning. [J]. Chinese journal on internet of things, 2024, 8(4): 140-155. DOI： 10.11959/j.issn.2096-3750.2024.00442.

摘要

随着物联网（IoT

Internet of things）设备的迅速普及，针对IoT设备的攻击频率和强度不断上升，因而持续更新安全机制以保障物联网设备的安全显得尤为重要。然而，随着公众隐私意识的增强，越来越多的数据集不再对外共享，形成数据“孤岛”现象，阻碍了物联网安全防护能力的提升。为了解决这一问题，提出了一种基于联邦强化学习的入侵检测方法，并通过医疗物联网（IoMT

Internet of medical things）和车联网（IoV

Internet of vehicles）场景下的两个数据集进行实验验证。为模拟真实环境，在每个边缘代理中设计了不平衡的流量样本分布，进而评估全局模型的检测精度和鲁棒性。采用双深度Q网络（DDQN

double deep Q-network）为边缘代理的强化学习框架，并通过准确率、精确率、召回率和F1分数对实验结果进行评估。实验结果表明，提出的方法具有良好的鲁棒性和检测精度。

Abstract

With the rapid proliferation of Internet of things (IoT) devices

the frequency and intensity of attacks targeting these devices are constantly increasing. Therefore

it's quite important that security mechanisms are continuously updated to ensure the safety of IoT devices. However

as public awareness of privacy grows

many datasets are no longer shared

leading to the emergence of data silos

which hinders the improvement of IoT security. To address this issue

a federated reinforcement learning-based intrusion detection method was proposed

and experiments were conducted using two datasets from the Internet of medical things (IoMT) and Internet of vehicles (IoV) scenarios. Imbalanced traffic sample distributions were designed for each edge agent to simulate a real-world environment

allowing for the evaluation of the detection accuracy and robustness of the global model. Double deep Q-network (DDQN) was employed as the reinforcement learning framework for the edge agents

and the experimental results were evaluated using accuracy

precision

recall

and F1-score. The results demonstrate that the proposed method exhibits strong robustness and detection accuracy.

关键词

联邦强化学习入侵检测物联网安全物联网

Keywords

federated reinforcement learningintrusion detectionIoT securityIoT

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