基于卷积神经网络的网络节点异常数据检测方法

异常数据检测是保障无线传感器网络节点数据准确性和可靠性的重要步骤。针对无线传感器网络节点异常数据检测问题，提出一种基于卷积神经网络的异常数据检测方法。该方法是对正常数据和注入故障后生成的异常数据进行归一化处理后映射成的灰度图片作为卷积神经网络的输入数据，并且基于LeNet-5卷积神经网络设计了合适的卷积层特征面及全连接层的参数，构造了3种新的卷积神经网络模型。该模型通过卷积层自主学习数据特征，解决了传统检测算法的性能容易受到相关阈值影响的问题。通过网络公开数据集进行模型测试，结果表明该方法具有很好的检测性能和较高的可靠性

A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network

Abnormal data detection is an important step to ensure the accuracy and reliability of node data in wireless sensor networks.A data classification method based on convolutional neural network was proposed to solve the problem of data anomaly detection in wireless sensor networks. Normal data and abnormal data generated after injection fault were 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 were designed by designing the parameters of the convolutional layer and the fully connected layer.Using this model, the problem that the performance of traditional detection algorithm is easily affected by relevant threshold is solved through self learning data characteristics of convolution layer. The experimental results show that this method has better detection performance and higher reliability.