Spiekermann,DanielKeller,JörgDemmler, DanielKrupka, DanielFederrath, Hannes2022-09-282022-09-282022978-3-88579-720-3https://dl.gi.de/handle/20.500.12116/39583The increasing number of network-based attacks like denial-of-service and ransomware have become a serious threat in nowadays digital infrastructures. Therefore, the monitoring of network communications and the classification of network packets is a critical process when protecting the environment. Modern techniques like deep learning aim to help the providers when detecting anomalies or attacks by learning details extracted from a network packet or a flow of packets. Most of these models are trained in networks without any kind of virtualisation, especially network virtualisation overlay environments are not investigated in detail. In this paper, we analyse the classification rate of a Convolutional Neural Network (CNN) faced with encapsulated packets. We evaluate this approach with a proof-of-concept based on a binary classification of a self-curated data-set.envirtual networksnetwork virtualisation overlaydeep learningneural networksnetwork traffic classification10.18420/inf2022_081617-5468