Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification

With the increasing prevalence of encrypted network traffic, cyber security analysts have been turning to machine learning (ML) techniques to elucidate the traffic on their networks. However, ML models can become stale as new traffic emerges that is outside of the distribution of the training set. I...

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Veröffentlicht in:	arXiv.org 2023-03
Hauptverfasser:	Jorgensen, Steven, Holodnak, John, Jensen, Dempsey, de Souza, Karla, Ananditha Raghunath, Rivet, Vernon, DeMoes, Noah, Alejos, Andrés, Wollaber, Allan
Format:	Artikel
Sprache:	eng
Schlagworte:	Communications traffic Computer Science - Cryptography and Security Computer Science - Learning Cybersecurity Datasets Encryption Environment models Flags Machine learning Traffic models Uncertainty Virtual private networks
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