基于卷积神经网络的无线传感器网络链路异常检测算法

doi:10.13229/j.cnki.jdxbgxb.20230224

Abstract

Abstract:

To accurately detect abnormal links in wireless sensor networks， a CNN based wireless sensor network link anomaly detection algorithm is proposed. Design network link anomaly detection function using concurrent multithreading technology， and establish a training model for network link anomaly detection using CNN. Input network link data， convolve network link information， extract network link feature vectors， and analyze network link abnormal behavior through down sampling function processing. Use vector mapping to represent the abnormal part vector， and complete classification detection through Softmax function classifier. The experimental results show that the proposed method can effectively improve the accuracy of link anomaly classification and detection， and it takes a short time.

Key words: wireless sensor network, convolution neural network, convolution operation, activation function, lower sampling layer, link characteristics

CLC Number:

TP391

Chao-lu TEMUR,Ya-ping ZHANG. Link anomaly detection algorithm for wireless sensor networks based on convolutional neural networks[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2295-2300.

Figures/Tables 4

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Link anomaly detection algorithm for wireless sensor networks based on convolutional neural networks

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