Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. (arXiv:2205.05628v1 [cs.CR])

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 known traffic
features can shift between networks and as new traffic emerges that is outside
of the distribution of the training set. In order to reliably adapt in this
dynamic environment, ML models must additionally provide contextualized
uncertainty quantification to their predictions, which has received little
attention …

application arxiv labeling learning machine machine learning network quantification traffic uncertainty