Opening the Deep Pandora Box: Explainable Traffic Classification
Cedric Beliard, Alessandro Finamore, Dario Rossi · 2020
Fostered by the tremendous success in the image recognition field, recently there has been a strong push for the adoption of Convolutional Neural Networks (CNN) in networks, especially at the edge, assisted by low-power hardware equipment (known as “tensor processing units”) for the acceleration of CNN-related computations. The availability of such hardware has reignited the interest for traffic classification approaches that are based on Deep Learning. However, unlike tree-based approaches that are easy to interpret, CNNs are in essence represented by a large number of weights, whose interpretation is particularly obscure for the human operators. Since human operators will need to deal, troubleshoot, and maintain these automatically learned models, that will replace the more easily human-readable heuristic rules of DPI classification engine, there is a clear need to open the “deep pandora box”, and make it easily accessible for network domain experts. In this demonstration, we shed light in the inference process of a commercial-grade classification engine dealing with hundreds of classes, enriching the classification workflow with tools to enable better understanding of the inner mechanics of both the traffic and the models.