基于深度强化学习模型融合的海洋气象传感器网络入侵检测方法

张文潇; 苏新; 顾依凌

doi:10.11959/j.issn.2096-3750.2025.00454

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基于深度强化学习模型融合的海洋气象传感器网络入侵检测方法

专题：卫星物联网 | 更新时间：2025-04-21

- 基于深度强化学习模型融合的海洋气象传感器网络入侵检测方法
- Intrusion detection based on deep reinforcement learning model fusion for maritime meteorological sensor networks
- 物联网学报 2025年9卷第1期页码：89-102
- 作者机构：
  
  河海大学信息科学与工程学院，江苏常州 213000
- 作者简介：
  
  [ "张文潇（2000‒ ），女，河海大学信息科学与工程学院硕士生，主要研究方向为入侵检测、边缘/雾计算等。" ]
  [ "苏新（1986‒ ），男，博士，河海大学信息科学与工程学院教授，主要研究方向为移动通信、边缘/雾计算、智慧海洋等。" ]
  [ "顾依凌（2001‒ ），女，河海大学信息科学与工程学院硕士生，主要研究方向为入侵检测、边缘/雾计算、嵌入式系统等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62371181);常州市政策引导类计划国际科技合作/港澳台科技合作项目(CZ20230029)
- DOI：10.11959/j.issn.2096-3750.2025.00454
  中图分类号： TN929.52;
- 收稿：2024-09-29，
  
  修回：2024-12-03，
  
  纸质出版：2025-03-30
- 稿件说明：
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张文潇,苏新,顾依凌.基于深度强化学习模型融合的海洋气象传感器网络入侵检测方法[J].物联网学报,2025,09(01):89-102.

ZHANG Wenxiao,SU Xin,GU Yiling.Intrusion detection based on deep reinforcement learning model fusion for maritime meteorological sensor networks[J].Chinese Journal on Internet of Things,2025,09(01):89-102.
张文潇,苏新,顾依凌.基于深度强化学习模型融合的海洋气象传感器网络入侵检测方法[J].物联网学报,2025,09(01):89-102. DOI： 10.11959/j.issn.2096-3750.2025.00454.

ZHANG Wenxiao,SU Xin,GU Yiling.Intrusion detection based on deep reinforcement learning model fusion for maritime meteorological sensor networks[J].Chinese Journal on Internet of Things,2025,09(01):89-102. DOI： 10.11959/j.issn.2096-3750.2025.00454.

摘要

海洋气象传感器网络（MMSN

maritime meteorological sensor network）有别于传统陆地组网，入侵检测任务在海洋气象传感器网络场景下面临着新的挑战。利用卫星通信技术设计一种海洋气象传感器网络卫星检测方法，分析海洋气象传感器网络的网络结构和特点。从算法和损失函数的角度入手，对提高入侵检测系统（IDS

intrusion detection system）检测性能的方法展开研究，提出了一种基于深度强化学习模型融合的海洋气象传感器网络入侵检测方法。首先，建立改进损失函数的轻量梯度提升机（LightGBM

light gradient boosting machine）、一维卷积神经网络（1D-CNN

1D conventional neural network）和二维卷积神经网络（2D-CNN

2D conventional neural network）分类器，综合提取海洋气象传感器网络入侵检测数据的时序特征和空间特征。其次，根据模型融合技术中的堆叠和平均原理，设计一个基于以上基分类器的模型融合方法，采纳基学习器的优势而规避其劣势，从而提高系统整体检测性能。最后，仿真实验结果表明，所提的入侵检测方法能够有效地提高入侵检测系统对少数类攻击数据的检测性能，并提高系统的稳健性。

Abstract

Maritime meteorological sensor networks (MMSN) differ from traditional land-based networks

presenting new challenges for intrusion detection tasks. A satellite-based detection method for maritime meteorological sensor networks was designed using satellite communication technology. The network structure and characteristics of maritime meteorological sensor networks were analyzed in this method. Research was conducted on improving the detection performance of intrusion detection systems (IDS) from the perspectives of algorithms and loss functions. A maritime meteorological sensor network intrusion detection method based on the fusion of deep reinforcement learning models was proposed. Firstly

light gradient boosting machine (LightGBM)

1D conventional neural network (1D-CNN)

and 2D conventional neural network (2D-CNN) classifiers with improved loss functions were established to comprehensively extract the temporal and spatial features of the intrusion detection data in maritime meteorological sensor networks. Secondly

a model fusion method was designed based on the stacking and averaging principles of model fusion technology. This method leveraged the strengths of the base classifiers and mitigated their weaknesses

thereby enhancing the overall system detection performance. Finally

simulation experiment results demonstrate that the proposed intrusion detection method can effectively improve the detection performance for a few types of attack data and enhance the robustness of the system.

关键词

Keywords

references

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