Unknown pattern extraction for statistical network protocol identification

Wang, Yu, Chen, Chao and Xiang, Yang 2015, Unknown pattern extraction for statistical network protocol identification, in LCN 2015: Proceedings of the IEEE Local Computer Networks 2015 Annual Conference, IEEE, Piscataway, N.J., pp. 506-509, doi: 10.1109/LCN.2015.7366364.

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Title Unknown pattern extraction for statistical network protocol identification
Author(s) Wang, YuORCID iD for Wang, Yu orcid.org/0000-0002-9807-2293
Chen, Chao
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Conference name IEEE Local Computer Networks. Annual Conference (40th : 2015 : Cleanwater Beach, Fla.))
Conference location Cleanwater Beach, Fla.
Conference dates 26-29 Oct. 2015
Title of proceedings LCN 2015: Proceedings of the IEEE Local Computer Networks 2015 Annual Conference
Editor(s) [Unknown]
Publication date 2015
Conference series IEEE Local Computer Networks Annual Conference
Start page 506
End page 509
Total pages 4
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) network protocol
machine learning
semi-supervised learning
constrained clustering
Summary The past decade has seen a lot of research on statistics-based network protocol identification using machine learning techniques. Prior studies have shown promising results in terms of high accuracy and fast classification speed. However, most works have embodied an implicit assumption that all protocols are known in advance and presented in the training data, which is unrealistic since real-world networks constantly witness emerging traffic patterns as well as unknown protocols in the wild. In this paper, we revisit the problem by proposing a learning scheme with unknown pattern extraction for statistical protocol identification. The scheme is designed with a more realistic setting, where the training dataset contains labeled samples from a limited number of protocols, and the goal is to tell these known protocols apart from each other and from potential unknown ones. Preliminary results derived from real-world traffic are presented to show the effectiveness of the scheme.
ISBN 9781467367714
Language eng
DOI 10.1109/LCN.2015.7366364
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Grant ID DP150103732
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084591

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