.png){kind=icon}
返回
Tree-RNN: Tree structural recurrent neural network for network traffic classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114363.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Network traffic classification plays an important role in network monitoring and network management. With the continuous development of network technology, traditional methods of traffic classification have more limitations in accuracy to deal with encrypted traffic. Fortunately, deep neural network (DNN) is an effective method for handling traffic classification due to its ability to learn inherent data features. However, this method generally classifies network traffic with only the single classifier, which makes it relatively less effective in some classes for the problem of large classification. In this paper, we propose a tree structural recurrent neural network (Tree-RNN), which divides a large classification into small classifications by using the tree structure. A specific classifier is set for each small classification after division. With multiple classifiers employed, Tree-RNN can complement each other in classification performance, and the problem of the single classifier is solved. Since multiple classifiers are all end-to-end frameworks, Tree-RNN can automatically learn the nonlinear relationship between input data and output data without feature extraction. To verify the validity of our model, we compare Tree-RNN with state-of-the-art methods using the ISCX public traffic dataset. Experimental results show that Tree-RNN can achieve higher performance in less training time. The average accuracy of Tree-RNN is 4.88% higher than other state-of-the-art methods, and it has higher average precision and average recall.
Keyword:
Network traffic classification
Deep learning
Recurrent neural network
Tree structure
End-to-end
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Passive browser identification with multi-scale Convolutional Neural Networks基于多尺度卷积神经网络的被动浏览器识别.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Deep-Full-Range: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection FrameworkDeep-Full-Range: 基于深度学习的网络加密流量分类和入侵检测框架IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏A mobile malware detection method using behavior features in network traffic一种基于网络流量行为特征的移动恶意软件检测方法
PREAI
分享收藏Exploratory study on Class Imbalance and solutions for Network Traffic ClassificationNEUROCOMPUTINGOAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏
查看更多内容