Standard
Early Application Identification. / Salamatian, Kave; Bernaille, L.; Teixeira, R.
2006. 64-75 CoNext 2006: 2nd International Conference on Future Networking Technologies.
Research output: Contribution to conference - Without ISBN/ISSN › Other
Harvard
Salamatian, K, Bernaille, L & Teixeira, R 2006, '
Early Application Identification', CoNext 2006: 2nd International Conference on Future Networking Technologies,
1/01/00 pp. 64-75.
APA
Salamatian, K., Bernaille, L., & Teixeira, R. (2006).
Early Application Identification. 64-75. CoNext 2006: 2nd International Conference on Future Networking Technologies.
Vancouver
Author
Salamatian, Kave ; Bernaille, L. ; Teixeira, R. /
Early Application Identification. CoNext 2006: 2nd International Conference on Future Networking Technologies.12 p.
Bibtex
@conference{a15795c0fb084879bb589fb79939322a,
title = "Early Application Identification",
author = "Kave Salamatian and L. Bernaille and R. Teixeira",
note = "This paper, following a series of papers on flow classification, proposes a very practical method for online network flows recognition and classification. It presents an approach based on non-supervised statistical machine learning that could realistically be implemented in real time in high-speed networks with only very limited information. The flow recognition is based only the packet size and directions of the first 5 to 7 packets of an applicative flow. In recognition of the simplicity and the practicality of the approach, this work received the Best Paper Award in the CoNext 2006 conference. RAE_import_type : Conference contribution RAE_uoa_type : Computer Science and Informatics; CoNext 2006: 2nd International Conference on Future Networking Technologies ; Conference date: 01-01-1900",
year = "2006",
month = dec,
day = "4",
language = "English",
pages = "64--75",
}
RIS
TY - CONF
T1 - Early Application Identification
AU - Salamatian, Kave
AU - Bernaille, L.
AU - Teixeira, R.
N1 - This paper, following a series of papers on flow classification, proposes a very practical method for online network flows recognition and classification. It presents an approach based on non-supervised statistical machine learning that could realistically be implemented in real time in high-speed networks with only very limited information. The flow recognition is based only the packet size and directions of the first 5 to 7 packets of an applicative flow. In recognition of the simplicity and the practicality of the approach, this work received the Best Paper Award in the CoNext 2006 conference. RAE_import_type : Conference contribution RAE_uoa_type : Computer Science and Informatics
PY - 2006/12/4
Y1 - 2006/12/4
M3 - Other
SP - 64
EP - 75
T2 - CoNext 2006: 2nd International Conference on Future Networking Technologies
Y2 - 1 January 1900
ER -
