{"title":"Multi-view multi-label network traffic classification based on MLP-Mixer neural network","authors":"","doi":"10.1016/j.comnet.2024.110746","DOIUrl":null,"url":null,"abstract":"<div><p>Network traffic classification is the basis of many network security applications and has received significant attention in the field of cyberspace security. Existing research on deep traffic analysis typically involves converting traffic data into images to extract spatial traffic features using Convolutional Neural Networks (CNNs). However, this approach ignores the semantic differences and details in the various packet structures. In this paper, we propose an MLP-Mixer based multi-view multi-label neural network for network traffic classification. Compared with the existing CNN-based methods, our method adopts the MLP-Mixer structure, which is more in line with the structure of the packet than the conventional convolution operation. In our method, one packet is divided into the packet header and the packet payload, together with the flow statistics of the packet as input from different views. We utilize a multi-label setting to learn different scenarios simultaneously to improve the classification performance by exploiting the correlations between different scenarios. We conduct experiments on three public datasets, and the experimental results show that our method can achieve superior performance. Code is available at <span><span>https://github.com/ZxuanDang/MV-ML-traffic-classification</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389128624005784","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Network traffic classification is the basis of many network security applications and has received significant attention in the field of cyberspace security. Existing research on deep traffic analysis typically involves converting traffic data into images to extract spatial traffic features using Convolutional Neural Networks (CNNs). However, this approach ignores the semantic differences and details in the various packet structures. In this paper, we propose an MLP-Mixer based multi-view multi-label neural network for network traffic classification. Compared with the existing CNN-based methods, our method adopts the MLP-Mixer structure, which is more in line with the structure of the packet than the conventional convolution operation. In our method, one packet is divided into the packet header and the packet payload, together with the flow statistics of the packet as input from different views. We utilize a multi-label setting to learn different scenarios simultaneously to improve the classification performance by exploiting the correlations between different scenarios. We conduct experiments on three public datasets, and the experimental results show that our method can achieve superior performance. Code is available at https://github.com/ZxuanDang/MV-ML-traffic-classification.
期刊介绍:
Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.