A robustness-enhanced traffic classification method in airborne network

被引：0
|
作者：
Lyu N. [1 ]
Zhou J. [1 ]
Chen Z. [1 ]
Liu P. [1 ]
Gao W. [1 ]
机构：
[1] School of Information and Navigation, Air Force Engineering University, Xi'an
来源：
Lyu, Na (lvnn2007@163.com) | 1600年 / Beijing University of Aeronautics and Astronautics (BUAA)卷 / 46期
基金：
中国国家自然科学基金;
关键词：
Airborne network; Deep learning; Feature extraction; Robustness; Traffic classification;
D O I：
10.13700/j.bh.1001-5965.2019.0475
中图分类号：
学科分类号：
摘要：
The highly dynamic and highly unstable characteristics of the airborne network make it difficult for traffic monitoring equipment to extract the complete data flow load characteristics within a limited monitoring period, thus limiting the application of the deep learning based traffic classification method. Aimed at this problem, a robustness-enhanced airborne network traffic classification method is proposed. First, data stream samples are mapped to gray vector sets by data preprocessing and missing sample processing methods. Then, the Robustness-Enhanced Long-term Recursive Convolutional neural Network (RE-LRCN) classification model is trained based on the complete traffic training set. Finally, in the online classification stage, the loading space features of packets-sample deficient data flows and timing features of data flows are extracted and the traffic is classified with the RE-LRCN model. The experiment results on the packets-sample deficient test set show that the proposed method can effectively suppress the deterioration of the accuracy of classification due to the missing of packet samples. © 2020, Editorial Board of JBUAA. All right reserved.
引用
收藏
页码：1237 / 1246
页数：9
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