Byte me: a case for byte accuracy in traffic classification

Authors:
Jeffrey Erman;Anirban Mahanti;Martin Arlitt
Affiliations:
University of Calgary, Canada;Indian Institute of Technology, Delhi, India;HP Labs, Palo Alto, USA and University of Calgary, Canada
Venue:
Proceedings of the 3rd annual ACM workshop on Mining network data
Year:
2007

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Abstract

Numerous network traffic classification approaches have recently been proposed. In general, these approaches have focused on correctly identifying a high percentage of total flows. However, on the Internet a small number of "elephant" flows contribute a significant amount of the traffic volume. In addition, some application types like Peer-to-Peer (P2P) and FTP contribute more elephant flows than other applications types like Chat. In this opinion piece, we discuss how evaluating a classifier on flow accuracy alone can bias the classification results. By not giving special attention to these traffic classes and their elephant flows in the evaluation of traffic classification approaches we might obtain significantly different performance when these approaches are deployed in operational networks for typical traffic classification tasks such as traffic shaping. We argue that byte accuracy must also be used when evaluating the accuracy of traffic classification algorithms.