Interactive, Visual Uncertainty Quantification

for Encrypted Network Traffic Situation Awareness

Harry X. Li, Allan Wollaber

Presented at VizSec21, Best Poster

Demo Video

As machine learning-based applications reach the hands of operators, the models often struggle to communicate the confidence of their predictions. Operators may end up blindly trusting a model, unaware that the model is only marginally confident or has never seen the input data during training. We present a visualization dashboard for encrypted network traffic labels that combines confidence sliders and visualizations to contextualize the model’s uncertainty.

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DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. Air Force. © 2021 Massachusetts Institute of Technology. Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.