基于ResNeXt的加密流分类方法

Li Yidan, Chen Yanli, Chen Runze, Yin Lan, Ruan Fangming
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引用次数: 1

摘要

加密流分类技术将流量数据根据不同的应用或不同的流量类型进行分类。监控网络流量安全、采集网络流量信息是网络通信安全的重要技术之一。鉴于此,本文提出了一种基于ResNeXt网络的加密流量分类方法。在数据预处理中去除流量中的以太网报头和有效负载,然后使用改进和简化的ResNeXt模型来识别加密的流量数据。该预处理方法可以大大减小输入数据的大小,节省时间,达到更高的精度。实验结果表明,该方法对“ICSX VPN-NonVPN”数据集中12种加密流量的分类准确率为98.58%,平均准确率为98.70%,召回率为98.49%,F1分数为0.9859。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Encryption Traffic Classification Method Based on ResNeXt
Encryption traffic classification technology classifies traffic data according to different applications or different traffic types. It is one of the most important technologies to monitor network traffic security and collect network traffic information. In view of this, this paper proposes an encrypted traffic classification method based on the ResNeXt network. Ethernet headers and payloads in the traffic are removed in data preprocessing, and then the improved and simplified ResNeXt model is used to identify encrypted traffic data. The preprocessing method can greatly reduce the size of input data, save time, and achieve higher accuracy. The experimental results show that the classification accuracy of the proposed method for 12 types of encrypted traffic in "ICSX VPN-NonVPN" data set is 98.58%, and the average accuracy rate, recall rate and F1 score are 98.70%, 98.49%, and 0.9859, respectively.
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