How many packets are most effective for early stage traffic identification: An experimental study
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Year
: 2014DOI: 10.1109/CC.2014.6969782
Keyword(s): Internet,learning (artificial intelligence),telecommunication traffic,crossover identification experiment,early stage traffic identification,feature extraction,machine learning model,packet size,traffic data sets,Feature extraction,Machine learning,Packet switching,Telecommunication network management,Telecommunication traffic,early stage traffic classification,feature extraction,machine learning
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How many packets are most effective for early stage traffic identification: An experimental study
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| contributor author | Peng Lizhi | |
| contributor author | Yang Bo | |
| contributor author | Chen Yuehui | |
| contributor author | Wu Tong | |
| date accessioned | 2020-03-13T00:31:07Z | |
| date available | 2020-03-13T00:31:07Z | |
| date issued | 2014 | |
| identifier issn | 1673-5447 | |
| identifier other | 6969782.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1149648 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | How many packets are most effective for early stage traffic identification: An experimental study | |
| type | Journal Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8332949 | |
| subject keywords | Internet | |
| subject keywords | learning (artificial intelligence) | |
| subject keywords | telecommunication traffic | |
| subject keywords | crossover identification experiment | |
| subject keywords | early stage traffic identification | |
| subject keywords | feature extraction | |
| subject keywords | machine learning model | |
| subject keywords | packet size | |
| subject keywords | traffic data sets | |
| subject keywords | Feature extraction | |
| subject keywords | Machine learning | |
| subject keywords | Packet switching | |
| subject keywords | Telecommunication network management | |
| subject keywords | Telecommunication traffic | |
| subject keywords | early stage traffic classification | |
| subject keywords | feature extraction | |
| subject keywords | machine learning | |
| identifier doi | 10.1109/CC.2014.6969782 | |
| journal title | Communications, China | |
| journal volume | 11 | |
| journal issue | 9 | |
| filesize | 1798209 | |
| citations | 0 |