A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network
A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network
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一种基于卷积神经网络的网络节点异常数据检测方法
DOI：
10.53106/199115992022063303004
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Shaohua Niu Yihao Zang
中科院分区：
文献类型：
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作者：
Xianhao Shen Xianhao Shen;Changhong Zhu Xianhao Shen;Yihao Zang Changhong Zhu;Shaohua Niu Yihao Zang
异常数据检测是保证无线传感器网络节点数据准确性和可靠性的重要一步。本文提出一种基于卷积神经网络的数据分类方法来解决无线传感器网络中的数据异常检测问题。首先，将注入故障后产生的正常数据和异常数据进行归一化并映射到灰度图像，作为卷积神经网络的输入数据。然后，在经典卷积神经网络的基础上，通过设计卷积层和全连接层的参数，设计了三种新的卷积神经网络模型。该模型通过卷积层自学习数据特征，解决了传统检测算法性能易受相关阈值影响的问题。实验结果表明，该方法具有较好的检测性能和较高的可靠性。
Abnormal data detection is an important step to ensure the accuracy and reliability of node data in wireless sensor networks. In this paper, a data classification method based on convolutional neural network is proposed to solve the problem of data anomaly detection in wireless sensor networks. First, Normal data and abnormal data generated after injection fault are normalized and mapped to gray image as input data of the convolutional neural network. Then, based on the classical convolution neural network, three new convolutional neural network models are designed by designing the parameters of the convolutional layer and the fully connected layer. This model solves the problem that the performance of traditional detection algorithm is easily affected by relevant threshold through self-learning data characteristics of convolution layer. The experimental results show that this method has better detection performance and higher reliability.