Airborne Network Traffic Identification Method under Small Training Samples

被引：0

作者：

Lyu N. ^{[1
]}

Zhou J. ^{[1
]}

Chen Z. ^{[1
]}

Chen W. ^{[2
]}

机构：

[1] School of Information and Navigation, PLA Air Force Engineering University, Xi'an

[2] School of Cybersecurity, Northwestern Polytechnical University, Xi'an

来源：

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2020年 / 38卷 / 05期

关键词：

Airborne network; Convolutional neural network; Traffic identification; Transfer learning;

D O I：

10.1051/jnwpu/20203851129

中图分类号：

学科分类号：

摘要：

Due to the high cost and difficulty of traffic data set acquisition and the high time sensitivity of traffic distribution, the machine learning-based traffic identification method is difficult to be applied in airborne network environment. Aiming at this problem, a method for airborne network traffic identification based on the convolutional neural network under small traffic samples is proposed. Firstly, the pre-training of the initial model for the convolutional neural network is implemented based on the complete data set in source domain, and then the retraining of the convolutional neural network is realized through the layer frozen based fine-tuning learning algorithm of convolutional neural network on the incomplete dataset in target domain, and the convolutional neural network model based feature representing transferring(FRT-CNN) is constructed to realize online traffic identification. The experiment results on the actual airborne network traffic dataset show that the proposed method can guarantee the accuracy of traffic identification under limited traffic samples, and the classification performance is significantly improved comparing with the existing small-sample learning methods. © 2020 Journal of Northwestern Polytechnical University.

引用

页码：1129 / 1138

页数：9

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