传感技术学报2024，Vol.37Issue(12)：2171-2176,6.DOI:10.3969/j.issn.1004-1699.2024.12.024
面向WSN网络拓扑突发流量不稳定状态识别的半监督学习模型构建
Semi Supervised Learning Model Construction for WSN Network Topology Burst Traffic Unstable State Identification
摘要
Abstract
The topology of wireless sensor networks has a high degree of self-organization and multi hop transmission uncertainty.When subjected to interference such as noise and attack behavior,it is easy to cause unstable network topology,reducing the reliability and se-curity of wireless sensor networks.Therefore,based on the construction of a semi supervised learning model,the identification of unstable states of burst traffic in WSN network topology is realized.Based on the AE model,a semi supervised learning model is estab-lished to process the data,the key features of the data are extracted,k-Means clustering algorithm is used to cluster,the low dimensional feature vectors of the key points of the data are obtained,histogram is used to mark the data feature categories,Euclidean distance is used to calculate the processed distance,and the identification of the unstable state of the burst traffic in the wireless sensor network to-pology is completed.The simulation results show that the proposed method identifies more than 90 abnormal traffic and 50 abnormal IP data,with a recognition false alarm rate of less than 10％,and has good recognition performance.关键词
无线传感网络/突发流量不稳定状态识别/半监督学习模型/直方图/k-Means均值聚类算法Key words
wireless sensor network/identification of unstable states of sudden traffic flow/semi supervised learning model/histogram/k-Means clustering algorithm分类
信息技术与安全科学引用本文复制引用
顾全,张薇..面向WSN网络拓扑突发流量不稳定状态识别的半监督学习模型构建[J].传感技术学报,2024,37(12):2171-2176,6.基金项目
江苏省教育科学"十三五"规划2020年度课题项目(B-b/2020/03/49) （B-b/2020/03/49）
江西省教育厅科学技术研究项目(GJJ2200642) （GJJ2200642）
