  Traffic classification, i.e. the identification of the type of applications
flowing in a network, is a strategic task for numerous activities (e.g.,
intrusion detection, routing). This task faces some critical challenges that
current deep learning approaches do not address. The design of current
approaches do not take into consideration the fact that networking hardware
(e.g., routers) often runs with limited computational resources. Further, they
do not meet the need for faithful explainability highlighted by regulatory
bodies. Finally, these traffic classifiers are evaluated on small datasets
which fail to reflect the diversity of applications in real-world settings.
  Therefore, this paper introduces a new Lightweight, Efficient and
eXplainable-by-design convolutional neural network (LEXNet) for Internet
traffic classification, which relies on a new residual block (for lightweight
and efficiency purposes) and prototype layer (for explainability). Based on a
commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain
the same accuracy as the best performing state-of-the-art neural network, while
providing the additional features previously mentioned. Moreover, we illustrate
the explainability feature of our approach, which stems from the communication
of detected application prototypes to the end-user, and we highlight the
faithfulness of LEXNet explanations through a comparison with post hoc methods.
