  The classification of internet traffic has become increasingly important due
to the rapid growth of today's networks and applications. The number of
connections and the addition of new applications in our networks causes a vast
amount of log data and complicates the search for common patterns by experts.
Finding such patterns among specific classes of applications is necessary to
fulfill various requirements in network analytics. Deep learning methods
provide both feature extraction and classification from data in a single
system. However, these networks are very complex and are used as black-box
models, which weakens the experts' trust in the classifications. Moreover, by
using them as a black-box, new knowledge cannot be obtained from the model
predictions despite their excellent performance. Therefore, the explainability
of the classifications is crucial. Besides increasing trust, the explanation
can be used for model evaluation gaining new insights from the data and
improving the model. In this paper, we present a visual interactive tool that
combines the classification of network data with an explanation technique to
form an interface between experts, algorithms, and data.
