Method of unknown protocol classification based on autoencoder
Aiming at the problem that a large number of unknown protocols exist in the Internet,which makes it very difficult to manage and maintain the network security,a classification and identification method of unknown protocols was proposed.Combined with the autoencoder technology and the improved K-mean...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020123/ |
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| _version_ | 1841539336178237440 |
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| author | Chunxiang GU Weisen WU Ya’nan SHI Guangsong LI |
| author_facet | Chunxiang GU Weisen WU Ya’nan SHI Guangsong LI |
| author_sort | Chunxiang GU |
| collection | DOAJ |
| description | Aiming at the problem that a large number of unknown protocols exist in the Internet,which makes it very difficult to manage and maintain the network security,a classification and identification method of unknown protocols was proposed.Combined with the autoencoder technology and the improved K-means clustering technology,the unknown protocol was classified and identified for the network traffic.The autoencoder was used to reduce dimensionality and select features of network traffic,clustering technology was used to classify the dimensionality reduction data unsupervised,and finally unsupervised recognition and classification of network traffic were realized.Experimental results show that the classification effect is better than the traditional K-means,DBSCAN,GMM algorithm,and has higher efficiency. |
| format | Article |
| id | doaj-art-309e3a4f02ec4fc6bda15fba9e71b7f9 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-309e3a4f02ec4fc6bda15fba9e71b7f92025-01-14T07:19:07ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-06-0141889759734850Method of unknown protocol classification based on autoencoderChunxiang GUWeisen WUYa’nan SHIGuangsong LIAiming at the problem that a large number of unknown protocols exist in the Internet,which makes it very difficult to manage and maintain the network security,a classification and identification method of unknown protocols was proposed.Combined with the autoencoder technology and the improved K-means clustering technology,the unknown protocol was classified and identified for the network traffic.The autoencoder was used to reduce dimensionality and select features of network traffic,clustering technology was used to classify the dimensionality reduction data unsupervised,and finally unsupervised recognition and classification of network traffic were realized.Experimental results show that the classification effect is better than the traditional K-means,DBSCAN,GMM algorithm,and has higher efficiency.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020123/unknown protocol classificationautoencoderunsupervised classificationfeature extraction |
| spellingShingle | Chunxiang GU Weisen WU Ya’nan SHI Guangsong LI Method of unknown protocol classification based on autoencoder Tongxin xuebao unknown protocol classification autoencoder unsupervised classification feature extraction |
| title | Method of unknown protocol classification based on autoencoder |
| title_full | Method of unknown protocol classification based on autoencoder |
| title_fullStr | Method of unknown protocol classification based on autoencoder |
| title_full_unstemmed | Method of unknown protocol classification based on autoencoder |
| title_short | Method of unknown protocol classification based on autoencoder |
| title_sort | method of unknown protocol classification based on autoencoder |
| topic | unknown protocol classification autoencoder unsupervised classification feature extraction |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020123/ |
| work_keys_str_mv | AT chunxianggu methodofunknownprotocolclassificationbasedonautoencoder AT weisenwu methodofunknownprotocolclassificationbasedonautoencoder AT yananshi methodofunknownprotocolclassificationbasedonautoencoder AT guangsongli methodofunknownprotocolclassificationbasedonautoencoder |