| Citation: | LYU Na, ZHOU Jiaxin, CHEN Zhuo, et al. A robustness-enhanced traffic classification method in airborne network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1237-1246. doi: 10.13700/j.bh.1001-5965.2019.0475(in Chinese)
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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.
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