network-traffic-classification

This paper presents the first empirical study evaluating Jev, a general-purpose decision model, for network traffic application classification using only early-flow packet features on the CESNET-QUICEXT-25 dataset. Labeled examples boost Jev's accuracy from 9.80% to 34.50%, but trained tree ensembles and GPT-5.6 Sol still outperform it, suggesting labeled in-context examples alone are insufficient to match dedicated classifiers.