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1 Ergebnisse
1
NeCSTGen: An approach for realistic network traffic generat..:
, In:
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
,
Meslet-Millet, Fabien
;
Mouysset, Sandrine
;
Chaput, Emmanuel
- p. 3108-3113 , 2022
Link:
https://doi.org/10.1109/GLOBECOM48099.2022.10000731
RT T1
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
: T1
NeCSTGen: An approach for realistic network traffic generation using Deep Learning
UL https://suche.suub.uni-bremen.de/peid=ieee-10000731&Exemplar=1&LAN=DE A1 Meslet-Millet, Fabien A1 Mouysset, Sandrine A1 Chaput, Emmanuel YR 2022 K1 Deep learning K1 Protocols K1 Recurrent neural networks K1 5G mobile communication K1 Aggregates K1 Telecommunication traffic K1 Data structures K1 Deep Learning K1 VAE K1 RNN K1 GMM K1 Network traffic generation SP 3108 OP 3113 LK http://dx.doi.org/https://doi.org/10.1109/GLOBECOM48099.2022.10000731 DO https://doi.org/10.1109/GLOBECOM48099.2022.10000731 SF ELIB - SuUB Bremen
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