摘要
无线传感网络的拓扑结构具有高度自组织性和多跳传输不确定性,在遭受噪声、攻击行为等干扰时,容易导致网络拓扑结构发生不稳定状态,降低无线传感网络的可靠性和安全性。为此,在构建半监督学习模型的基础上实现WSN网络拓扑突发流量不稳定状态的识别。根据AE模型建立半监督学习模型对数据进行处理,提取数据关键特征,利用k-Means均值聚类算法进行聚类,获取数据关键点的低维特征向量,采用直方图标记数据特征类别,利用欧氏距离对处理后的距离进行计算,完成无线传感网络拓扑突发流量不稳定状态的识别。仿真结果表明:所提方法识别出的异常流量以及异常IP数据量分别在90和50个以上,识别误报率低于10%,具有良好的识别效果。
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.
作者
顾全
张薇
GU Quan;ZHANG Wei(Yancheng Electromechanical Branch,Jiangsu Union Technical Institute,Yancheng Jiangsu 224000,China;Software College,East China Jiaotong University,Nanchang Jiangxi 330013,China)
出处
《传感技术学报》
CSCD
北大核心
2024年第12期2171-2176,共6页
Chinese Journal of Sensors and Actuators
基金
江苏省教育科学“十三五”规划2020年度课题项目(B-b/2020/03/49)
江西省教育厅科学技术研究项目(GJJ2200642)。
