Won-Ju Eom, Yeong-Jun Song, Chang-hoon Park, Jeong-Keun Kim, Geon-Hwan Kim, You-Ze Cho
{"title":"基于集成学习的软件定义网络流量分类","authors":"Won-Ju Eom, Yeong-Jun Song, Chang-hoon Park, Jeong-Keun Kim, Geon-Hwan Kim, You-Ze Cho","doi":"10.1109/ICAIIC51459.2021.9415187","DOIUrl":null,"url":null,"abstract":"Accurate network traffic classification is essential for network management. However, existing network traffic classification methods cannot meet the demand of real networks in terms of classification performance, user privacy, latency, and control overhead. Thus, a machine learning-based approach has been used for network traffic classification. In this paper, we propose a network traffic classification framework using software-defined network (SDN) architecture. The proposed framework is entirely located in the network controller; thus, we can leverage the superior computational capacity, global visibility, and programmability of the SDN controller to realize real-time, adaptive, and accurate traffic classification. We also apply four ensemble algorithms and analyze their classification performance in terms of accuracy, precision, recall, F1-score, training time, and classification time. The experimental results reveal that ensemble model-based network traffic classifiers outperform other classifiers based on the proposed framework and the real-world network traffic dataset. Notably, the LightGBM model achieves the best classification performance.","PeriodicalId":432977,"journal":{"name":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Network Traffic Classification Using Ensemble Learning in Software-Defined Networks\",\"authors\":\"Won-Ju Eom, Yeong-Jun Song, Chang-hoon Park, Jeong-Keun Kim, Geon-Hwan Kim, You-Ze Cho\",\"doi\":\"10.1109/ICAIIC51459.2021.9415187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate network traffic classification is essential for network management. However, existing network traffic classification methods cannot meet the demand of real networks in terms of classification performance, user privacy, latency, and control overhead. Thus, a machine learning-based approach has been used for network traffic classification. In this paper, we propose a network traffic classification framework using software-defined network (SDN) architecture. The proposed framework is entirely located in the network controller; thus, we can leverage the superior computational capacity, global visibility, and programmability of the SDN controller to realize real-time, adaptive, and accurate traffic classification. We also apply four ensemble algorithms and analyze their classification performance in terms of accuracy, precision, recall, F1-score, training time, and classification time. The experimental results reveal that ensemble model-based network traffic classifiers outperform other classifiers based on the proposed framework and the real-world network traffic dataset. Notably, the LightGBM model achieves the best classification performance.\",\"PeriodicalId\":432977,\"journal\":{\"name\":\"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"volume\":\"111 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIIC51459.2021.9415187\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIC51459.2021.9415187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Network Traffic Classification Using Ensemble Learning in Software-Defined Networks
Accurate network traffic classification is essential for network management. However, existing network traffic classification methods cannot meet the demand of real networks in terms of classification performance, user privacy, latency, and control overhead. Thus, a machine learning-based approach has been used for network traffic classification. In this paper, we propose a network traffic classification framework using software-defined network (SDN) architecture. The proposed framework is entirely located in the network controller; thus, we can leverage the superior computational capacity, global visibility, and programmability of the SDN controller to realize real-time, adaptive, and accurate traffic classification. We also apply four ensemble algorithms and analyze their classification performance in terms of accuracy, precision, recall, F1-score, training time, and classification time. The experimental results reveal that ensemble model-based network traffic classifiers outperform other classifiers based on the proposed framework and the real-world network traffic dataset. Notably, the LightGBM model achieves the best classification performance.