北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (7): 1237-1246.doi: 10.13700/j.bh.1001-5965.2019.0475

• 论文 • 上一篇    下一篇

一种鲁棒性增强的机载网络流量分类方法

吕娜, 周家欣, 陈卓, 刘鹏飞, 高维廷   

  1. 空军工程大学 信息与导航学院, 西安 710077
  • 收稿日期:2019-09-02 发布日期:2020-07-18
  • 通讯作者: 吕娜 E-mail:lvnn2007@163.com
  • 作者简介:吕娜 女,博士,教授,博士生导师。主要研究方向:航空数据链。
    周家欣 男,硕士研究生。主要研究方向:军事航空通信。
  • 基金资助:
    国家自然科学基金(61701521,61703427)

A robustness-enhanced traffic classification method in airborne network

LYU Na, ZHOU Jiaxin, CHEN Zhuo, LIU Pengfei, GAO Weiting   

  1. School of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
  • Received:2019-09-02 Published:2020-07-18
  • Supported by:
    National Natural Science Foundation of China (61701521,61703427)

摘要: 针对机载网络高度动态、高度不稳定造成流量监测设备难以在有限的监测周期内完成完整数据流负载特征的提取,限制了基于深度学习的流量分类方法的应用问题,提出了一种鲁棒性增强的机载网络流量分类方法。通过数据预处理及缺失样本处理方法将数据流映射为灰度矢量集合,基于完整的数据流训练数据集实现鲁棒性增强的长时递归卷积神经网络(RE-LRCN)分类模型的训练,在线上分类阶段,通过分类模型实现样本缺失数据流负载空间特征及数据流时序特征的提取,并进行数据流分类。通过在数据包缺失的流量测试数据集上的实验结果表明,所提方法可以有效抑制数据包缺失对分类准确性能的恶化。

关键词: 机载网络, 流量分类, 深度学习, 特征提取, 鲁棒性

Abstract: 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.

Key words: airborne network, traffic classification, deep learning, feature extraction, robustness

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