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Is our Ground-Truth for Traffic Classification Reliable?

Valentín Carela-Español, Tomasz Bujlow, and Pere Barlet-Ros

Proceedings of the 15th Passive and Active Measurement Conference (PAM 2014), Proceedings Series: Lecture Notes in Computer Science 8362, pp. 98 - 108, Springer International Publishing Switzerland, Los Angeles, USA, March 2014, DOI: 10.1007/978-3-319-04918-2_10.


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.

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