Training traffic classifiers with arbitrary packet sets

Runxin Wang, Lei Shi, Brendan Jennings

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Many existing machine learning based traffic classifiers require the first five packets in traffic flows to perform traffic classification. In this work, we investigate the flexibility of using arbitrary sets of packets to train traffic classifiers. Such classifiers could be used as auxiliary classifiers that would function in cases where some packets in flows are unavailable, possibly due to packet losses/retransmissions. Moreover, they could be used to mitigate the issue that payload mutation techniques are used by some malicious applications to evade classification. Experimental results show that with using some packet sets, our classifier produces comparable accuracy to the classifier using the first five packets in flows.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Communications Workshops, ICC 2013
Pages1314-1318
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Communications Workshops, ICC 2013 - Budapest, Hungary
Duration: 09 Jun 201313 Jun 2013

Publication series

Name2013 IEEE International Conference on Communications Workshops, ICC 2013

Conference

Conference2013 IEEE International Conference on Communications Workshops, ICC 2013
Country/TerritoryHungary
CityBudapest
Period09/06/201313/06/2013

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