How many packets are most effective for early stage traffic identification: An experimental study
ناشر:
سال
: 2014شناسه الکترونیک: 10.1109/CC.2014.6969782
کلیدواژه(گان): 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 | http://libsearch.um.ac.ir:80/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 |