Abstract
The validation of the different proposals in the traffic classification literature is a controversial issue. Usually, these works base their results on a ground-truth built from private datasets and labeled by techniques of unknown reliability. This makes the validation and comparison with other solutions an extremely difficult task. This paper aims to be a first step towards addressing the validation and trustworthiness problem of network traffic classifiers. We perform a comparison between 6 well-known DPI-based techniques, which are frequently used in the literature for ground-truth generation. In order to evaluate these tools we have carefully built a labeled dataset of more than 500 000 flows, which contains traffic from popular applications. Our results present PACE, a commercial tool, as the most reliable solution for ground-truth generation. However, among the open-source tools available, NDPI and especially Libprotoident, also achieve very high precision, while other, more frequently used tools (e.g., L7-filter) are not reliable enough and should not be used for ground-truth generation in their current form.
This research was funded by the Spanish Ministry of Economy and Competitiveness under contract TEC2011-27474 (NOMADS project), by the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya (ref. 2009SGR-1140) and by the European Regional Development Fund (ERDF).
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Carela-Español, V., Bujlow, T., Barlet-Ros, P. (2014). Is Our Ground-Truth for Traffic Classification Reliable?. In: Faloutsos, M., Kuzmanovic, A. (eds) Passive and Active Measurement. PAM 2014. Lecture Notes in Computer Science, vol 8362. Springer, Cham. https://doi.org/10.1007/978-3-319-04918-2_10
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DOI: https://doi.org/10.1007/978-3-319-04918-2_10
Publisher Name: Springer, Cham
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