Abstract
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.
Highlights
21: Tk = r × Tk 22: end if 23: end for 24: Sopt
[11] WANG Wei, ZHU Ming, WANG Jinlin, et al End⁃to⁃End Encrypted Traffic Classification with One Dimensional Convolution Neural Networks[ C] ∥2017 IEEE International Conference on Intelligence and Security Informatics( ISI), 2017 [12] 王勇, 周慧怡, 俸皓, 等. 基于深度卷积神经网络的网络流量分类方法[ J] . 通信学报, 2018, 39(1) : 14⁃23 WANG Yong, ZHOU Huiyi, FENG Hao, et al Network Traffic Classification Method Basing on CNN[ J]
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
Summary
21: Tk = r × Tk 22: end if 23: end for 24: Sopt 冻结的参数微调,优化了预训练 CNN 面向目标领域 的特征提取能力,在一定程度上提升了整体准确率, 其收敛值达到 75% 以上。 本文所提 FRT⁃CNN 方法 则通过逐层解冻结的 CNN 微调算法,进一步扩展了 重训练的范围。 由于在 FT⁃CNN 中待微调层共有 3 层( C1、FC1、FC2) ,因此在逐层解冻结的微调过程 中,整体准确率变化呈 3 个阶段,第一阶段为第 1 至 第 轮左右,为 C1 层微调阶段,整体准确率收敛于 65%左右;第二阶段为第 轮至第 轮左右, 为 FC1 层微调阶段,整体准确率收敛值提升至 70% 左 右;第三阶段为 轮至 60 轮,为 FC2 层微调阶段, 整体准确率最终收敛于 82%左右。 A Survey of Techniques for Internet Traffic Classification Using Machine Learning[ J] .
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