Wang, Yu, Chen, Chao, and Xiang, Yang (2015) Unknown pattern extraction for statistical network protocol identification. In: Proceedings of the IEEE Conference on Local Computer Networks. pp. 506-509. From: LCN 2015: 40th IEEE Conference on Local Computer Networks, 26-29 October 2016, Clearwater Beach, FL, USA.

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Abstract

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.

Item ID:	64418
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4673-6770-7
Keywords:	network protocol; machine learning; semi-supervised learning; constrained clustering
Copyright Information:	© 2015 IEEE
Date Deposited:	06 Oct 2020 23:26
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080303 Computer System Security @ 50% 08 INFORMATION AND COMPUTING SCIENCES > 0805 Distributed Computing > 080503 Networking and Communications @ 50%
SEO Codes:	89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 100%
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