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

共 18 条

[11]

WANG Wei, ZHU Ming, WANG Jinlin, Et al., End-to-End Encrypted Traffic Classification with One Dimensional Convolution Neural Networks, 2017 IEEE International Conference on Intelligence and Security Informatics(ISI), (2017)

[12]

WANG Yong, ZHOU Huiyi, FENG Hao, Et al., Network Traffic Classification Method Basing on CNN, Journal on Communications, 39, 1, pp. 14-23, (2018)

[13]

PAN Sinno Jialin, YANG Qiang, A Survey on Transfer Learning, IEEE Trans on Knowledge & Data Engineering, 22, 10, pp. 1345-1359, (2010)

[14]

SHIN H C, ROTH H R, GAO M, Et al., Deep Convolutional Neural Networks for Computer Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning, IEEE Trans on Medical Imaging, 1, 1, pp. 1285-1298, (2016)

[15]

ZHOU Feiyan, JIN Linpeng, DONG Jun, Review of Convolutional Neural Network, Chinese Journal of Computers, 40, 6, pp. 1-23, (2017)

[16]

LI Qin, SHI Wei, SUN Jieping, Et al., The Research of Network Traffic Identification Based on Convolutional Neural Network, Journal of Sichuan University, 54, 5, pp. 959-964, (2017)

[17]

SHARIF Razavian A, AZIZPOUR H, SULLIVAN J, Et al., CNN Feature Off-the-Shelf: an Astounding Baseline for Recognition, Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806-813, (2014)

[18]

JASON Yosinski, JEFF Clune, YOSHUA Bengio, Et al., How Transferable Are Features in Deep Neural Networks?, Advances in Neural Information Processing System 27, pp. 3320-3328, (2014)

← 1 2 →