{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T10:25:54Z","timestamp":1769941554484,"version":"3.49.0"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072336"],"award-info":[{"award-number":["62072336"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"New Generation Artificial Intelligence Technology Major Project of Tianjin","award":["19ZXZNGX00080"],"award-info":[{"award-number":["19ZXZNGX00080"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Network traffic classification is of fundamental importance to a wide range of network activities, such as security monitoring, accounting, quality of service and forecasting for long-term provisioning purposes. This task has been increasingly implemented using machine learning methods due to the inability of conventional approaches to accommodate the increasing use of encryption. However, the application of machine learning methods to network traffic classification based on sampled NetFlow data is poorly developed despite the fact that NetFlow is a widely extended monitoring solution routinely employed by network operators. This study addresses this issue by proposing a network traffic classification module using NetFlow data in conjunction with a deep neural network. The performance of the proposed classification module is demonstrated by its application to two real-world datasets, and an average classification accuracy of 95% is obtained for $\\sim $1.4 million test cases. Moreover, the performance of the proposed classifier is demonstrated to be superior to three other state-of-the-art classifiers. Accordingly, the proposed module represents a promising alternative for network traffic classification.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac049","type":"journal-article","created":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T19:09:28Z","timestamp":1650740968000},"page":"1882-1892","source":"Crossref","is-referenced-by-count":12,"title":["Network Traffic Classification Based On A Deep Learning Approach Using NetFlow Data"],"prefix":"10.1093","volume":"66","author":[{"given":"Zhang","family":"Long","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Computer Virus Prevention and Control Technology , Department of Computer Science and Engineering, Tianjin University of technology, No 319 Binshui West Road, Tianjin, China , 300384"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wang","family":"Jinsong","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Computer Virus Prevention and Control Technology , Department of Computer Science and Engineering, Tianjin University of technology, No 319 Binshui West Road, Tianjin, China , 300384"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"2023081805144801300_ref1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.jnca.2017.11.007","article-title":"Multi-classification approaches for classifying mobile app traffic","volume":"103","author":"Aceto","year":"2018","journal-title":"Journal of Network and Computer Applications"},{"key":"2023081805144801300_ref2","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2019.106944","article-title":"Mimetic: Mobile encrypted traffic classification using multimodal deep learning","volume":"165","author":"Aceto","year":"2019","journal-title":"Computer Networks"},{"key":"2023081805144801300_ref3","doi-asserted-by":"crossref","first-page":"2916","DOI":"10.1109\/TIFS.2019.2911156","article-title":"Hedge: efficient traffic classification of encrypted and compressed packets","volume":"14","author":"Casino","year":"2019","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"2023081805144801300_ref4","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1080\/23742917.2017.1321891","article-title":"Comparison of machine-learning algorithms for classification of vpn network traffic flow using time-related features","volume":"1","author":"Bagui","year":"2017","journal-title":"Journal of Cyber Security Technology"},{"key":"2023081805144801300_ref5","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/TNN.2006.883010","article-title":"Bayesian neural networks for internet traffic classification","volume":"18","author":"Auld","year":"2007","journal-title":"IEEE Trans. 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