Research on network traffic classification based on machine learning and deep learning
With the continuous development of Internet technology and the continuous expansion of network scale, there are many different types of applications , and various new applications have endlessly emerged.In order to ensure the quality of service (QoS) and ensure network security, accurate and fast tr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2021-03-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021052/ |
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| _version_ | 1841529077743222784 |
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| author | Yue GU Dan LI Kaihui GAO |
| author_facet | Yue GU Dan LI Kaihui GAO |
| author_sort | Yue GU |
| collection | DOAJ |
| description | With the continuous development of Internet technology and the continuous expansion of network scale, there are many different types of applications , and various new applications have endlessly emerged.In order to ensure the quality of service (QoS) and ensure network security, accurate and fast traffic classification is an urgent problem for both operators and network managers.Firstly, the problem definition and performance metrics of network traffic classification were given.Then, the traffic classification methods based on machine learning and deep learning were introduced respectively, the advantages and disadvantages of these methods were analyzed, and the existing problems were expounded.Next, the related work by focusing on the three problems encountered elaborated and analyzed in traffic classification when considering online deployment: dataset, zero-day application identification and the cost of online deployment, and further discusses the challenges faced by the current network traffic classification researches.Finally, the next research direction of network traffic classification was prospected. |
| format | Article |
| id | doaj-art-2c39f3e34f97471a96b734aa47c76924 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2021-03-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-2c39f3e34f97471a96b734aa47c769242025-01-15T03:26:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012021-03-013710511359807234Research on network traffic classification based on machine learning and deep learningYue GUDan LIKaihui GAOWith the continuous development of Internet technology and the continuous expansion of network scale, there are many different types of applications , and various new applications have endlessly emerged.In order to ensure the quality of service (QoS) and ensure network security, accurate and fast traffic classification is an urgent problem for both operators and network managers.Firstly, the problem definition and performance metrics of network traffic classification were given.Then, the traffic classification methods based on machine learning and deep learning were introduced respectively, the advantages and disadvantages of these methods were analyzed, and the existing problems were expounded.Next, the related work by focusing on the three problems encountered elaborated and analyzed in traffic classification when considering online deployment: dataset, zero-day application identification and the cost of online deployment, and further discusses the challenges faced by the current network traffic classification researches.Finally, the next research direction of network traffic classification was prospected.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021052/QoSnetwork securitytraffic classificationdata collectionzero-day application identification |
| spellingShingle | Yue GU Dan LI Kaihui GAO Research on network traffic classification based on machine learning and deep learning Dianxin kexue QoS network security traffic classification data collection zero-day application identification |
| title | Research on network traffic classification based on machine learning and deep learning |
| title_full | Research on network traffic classification based on machine learning and deep learning |
| title_fullStr | Research on network traffic classification based on machine learning and deep learning |
| title_full_unstemmed | Research on network traffic classification based on machine learning and deep learning |
| title_short | Research on network traffic classification based on machine learning and deep learning |
| title_sort | research on network traffic classification based on machine learning and deep learning |
| topic | QoS network security traffic classification data collection zero-day application identification |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021052/ |
| work_keys_str_mv | AT yuegu researchonnetworktrafficclassificationbasedonmachinelearninganddeeplearning AT danli researchonnetworktrafficclassificationbasedonmachinelearninganddeeplearning AT kaihuigao researchonnetworktrafficclassificationbasedonmachinelearninganddeeplearning |