Fusion reconstruction mechanism and contrast learning method for WSN abnormal node detection

YE Miao; CHENG Jin; HUANG Yuan; JIANG Qiuxiang; WANG Yong

doi:10.11959/j.issn.1000-436x.2024167

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Fusion reconstruction mechanism and contrast learning method for WSN abnormal node detection

Papers | 更新时间：2024-10-10

- Fusion reconstruction mechanism and contrast learning method for WSN abnormal node detection
- Journal on Communications Vol. 45, Issue 9, Pages: 153-169(2024)
- 作者机构：
  
  桂林电子科技大学广西无线宽带通信与信号处理重点实验室，广西桂林 541004
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62161006);The subsidization of Innovation Project of Guangxi Graduate Education(YCSW2023310);Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing(GXKL06220110)
- DOI：10.11959/j.issn.1000-436x.2024167
  CLC： TP393
- Received：10 April 2024，
  
  Revised：2024-08-20，
  
  Published：25 September 2024
- 稿件说明：
移动端阅览
叶苗,程锦,黄源等.面向WSN异常节点检测的融合重构机制与对比学习方法[J].通信学报,2024,45(09):153-169.

YE Miao,CHENG Jin,HUANG Yuan,et al.Fusion reconstruction mechanism and contrast learning method for WSN abnormal node detection[J].Journal on Communications,2024,45(09):153-169.
叶苗,程锦,黄源等.面向WSN异常节点检测的融合重构机制与对比学习方法[J].通信学报,2024,45(09):153-169. DOI： 10.11959/j.issn.1000-436x.2024167.

YE Miao,CHENG Jin,HUANG Yuan,et al.Fusion reconstruction mechanism and contrast learning method for WSN abnormal node detection[J].Journal on Communications,2024,45(09):153-169. DOI： 10.11959/j.issn.1000-436x.2024167.

摘要

针对无线传感器网络（WSN）异常检测中的自监督学习异常检测方法需要解决负例样本信息表示单一缺乏多样性和提取WSN节点采集到的多模态数据时空特征不够充分影响异常检测性能的问题。对此提出了一种结合对比学习和重构机制的无线传感器网络异常节点检测方法。首先，通过设计一种对比学习策略为重构机制模型提供足够充足的正负例样本，并结合生成对抗网络（GAN）生成具有多样性特性的负例样本；其次，设计了一种基于多头注意力机制和图神经网络的双层时空特征提取模块。通过在实际公开数据集上的系列对比实验及其实验结果表明，所提方法相比于传统异常检查方法和最近的图神经网络方法具有更好的精确率和召回率。

Abstract

To tackle the defects of self-supervised learning anomaly detection methods for wireless sensor network (WSN) need to address the problems of single negative sample types and lack of diversity

as well as insufficient extraction of spatiotemporal features from multimodal data of wireless sensor network nodes. To address these challenges

a wireless sensor network anomaly node detection method that combines contrastive learning and reconstruction mechanisms was proposed. Firstly

this method provided sufficient positive and negative example information representation for the reconstruction model by using contrastive learning methods

and combined with generative adversarial network (GAN) to generate negative examples with diverse characteristics. Secondly

a dual layer spatiotemporal feature extraction module based on multi-head attention and graph neural network was designed. Through a series of comparative experiments on actual public datasets and their experimental results

it is shown that the method designed has better accuracy and recall compared to traditional anomaly detection methods and recent graph neural network methods.

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Keywords

references

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