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

no code implementations • 11 May 2022 • Steven Jorgensen, John Holodnak, Jensen Dempsey, Karla de Souza, Ananditha Raghunath, Vernon Rivet, Noah DeMoes, Andrés Alejos, Allan Wollaber

We also present an ML framework that is designed to rapidly train with modest data requirements and provide both calibrated, predictive probabilities as well as an interpretable "out-of-distribution" (OOD) score to flag novel traffic samples.

BIG-bench Machine Learning Uncertainty Quantification