Is Our Ground-Truth for Traffic Classification Reliable?

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.