Network protocol identification based on active learning and SVM algorithm
Obtaining qualified training data for protocol identif ion generally requires domain experts to be involved,which is time-consuming and laborious.A novel approach for network protocol identification based on active learning and SVM algorithm was proposed.The experimental evaluations on real-world ne...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2013-10-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.10.016/ |
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| _version_ | 1841539818380591104 |
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| author | Yi-peng WANG Xiao-chun YUN Yong-zheng ZHANG Shu-hao LI |
| author_facet | Yi-peng WANG Xiao-chun YUN Yong-zheng ZHANG Shu-hao LI |
| author_sort | Yi-peng WANG |
| collection | DOAJ |
| description | Obtaining qualified training data for protocol identif ion generally requires domain experts to be involved,which is time-consuming and laborious.A novel approach for network protocol identification based on active learning and SVM algorithm was proposed.The experimental evaluations on real-world network traces show this approach can accurately and efficiently classify the target network protocol from mixed Internet traffic,and meanwhile display a sig-nificant reduction in the number of labeled samples.Therefore,this approach can be employed as an auxiliary tool for analyzing unknown protocols in real-world environment. |
| format | Article |
| id | doaj-art-8235b0a0248442ca82925f00144aff79 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2013-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8235b0a0248442ca82925f00144aff792025-01-14T06:41:31ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2013-10-013413514259675917Network protocol identification based on active learning and SVM algorithmYi-peng WANGXiao-chun YUNYong-zheng ZHANGShu-hao LIObtaining qualified training data for protocol identif ion generally requires domain experts to be involved,which is time-consuming and laborious.A novel approach for network protocol identification based on active learning and SVM algorithm was proposed.The experimental evaluations on real-world network traces show this approach can accurately and efficiently classify the target network protocol from mixed Internet traffic,and meanwhile display a sig-nificant reduction in the number of labeled samples.Therefore,this approach can be employed as an auxiliary tool for analyzing unknown protocols in real-world environment.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.10.016/network securityprotocol identificationactive learningnetwork tracessupport vector machine |
| spellingShingle | Yi-peng WANG Xiao-chun YUN Yong-zheng ZHANG Shu-hao LI Network protocol identification based on active learning and SVM algorithm Tongxin xuebao network security protocol identification active learning network traces support vector machine |
| title | Network protocol identification based on active learning and SVM algorithm |
| title_full | Network protocol identification based on active learning and SVM algorithm |
| title_fullStr | Network protocol identification based on active learning and SVM algorithm |
| title_full_unstemmed | Network protocol identification based on active learning and SVM algorithm |
| title_short | Network protocol identification based on active learning and SVM algorithm |
| title_sort | network protocol identification based on active learning and svm algorithm |
| topic | network security protocol identification active learning network traces support vector machine |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2013.10.016/ |
| work_keys_str_mv | AT yipengwang networkprotocolidentificationbasedonactivelearningandsvmalgorithm AT xiaochunyun networkprotocolidentificationbasedonactivelearningandsvmalgorithm AT yongzhengzhang networkprotocolidentificationbasedonactivelearningandsvmalgorithm AT shuhaoli networkprotocolidentificationbasedonactivelearningandsvmalgorithm |