DocumentCode :
694603
Title :
LAYSEE: Learn-as-you-see traffic classifier
Author :
Tongaonkar, Alok ; Iliofotou, Marios ; Keralapura, Ram
Author_Institution :
Narus Inc., Sunnyvale, CA, USA
fYear :
2013
fDate :
14-19 April 2013
Firstpage :
25
Lastpage :
26
Abstract :
The ability to classify all traffic that traverses a network is a critical aspect of network management. Signature based traffic classifiers are widely used to provide that capability. The state of the art classifiers rely on static, manual, and tedious approach of protocol reverse engineering to obtain signatures. However, the explosion of never-seen-before applications on the internet has resulted in a drastic reduction in the effectiveness of such systems. To overcome these limitations, we have developed a novel system, called Learn-As-You-SEE (LAYSEE), that aims to provide dynamic, automated, and exhaustive application identification. Our system automatically extracts signatures from network traffic by leveraging the benefits of packet content signature inference techniques and sophisticated behavioral-based analysis. These signatures are used for classifying subsequent traffic.
Keywords :
Internet; computer network management; pattern classification; protocols; reverse engineering; telecommunication traffic; Internet; LAYSEE; automated application identification; behavioral-based analysis; dynamic application identification; exhaustive application identification; learn-as-you-see traffic classifier; network management; network traffic; never-seen-before applications; packet content signature inference techniques; protocol reverse engineering; signature based traffic classifiers; signature extraction; Classification algorithms; Feature extraction; Internet; Manuals; Payloads; Ports (Computers); Protocols;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Communications Workshops (INFOCOM WKSHPS), 2013 IEEE Conference on
Conference_Location :
Turin
Print_ISBN :
978-1-4799-0055-8
Type :
conf
DOI :
10.1109/INFCOMW.2013.6970707
Filename :
6970707
Link To Document :
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