Source code of Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine Learning

Zenodo (CERN European Organization for Nuclear Research) 2026 Open access

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Authors: Akem Aristide, Guillaume Fraysse
DOI: 10.5281/zenodo.18462939
Cited by: 0

Abstract

Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine Learning.This repository contains the source code for our work on Encrypted Traffic Classification (ETC) in programmable switches with P4 and Machine Learning, appearing in the Proceedings of IEEE/IFIP NOMS 2024, 6–10 May 2024, Seoul, South Korea, and in the International Journal of Network Management, Wiley, vol. 35, no. 1, January 2025.

Fields of Study & Subjects

Internet Traffic Analysis and Secure E-voting Software-Defined Networks and 5G Network Packet Processing and Optimization

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
2 February 2026