# Predicting Process Name from Network Data

arXiv: https://arxiv.org/abs/2109.03328 · HTML: https://mlsanity.com/paper/2109.03328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 1 of 1)

- [Browser Fingerprinting: A survey](https://mlsanity.com/paper/1905.01051/) (2019)

## Abstract (arXiv)

The ability to identify applications based on the network data they generate could be a valuable tool for cyber defense. We report on a machine learning technique capable of using netflow-like features to predict the application that generated the traffic. In our experiments, we used ground-truth labels obtained from host-based sensors deployed in a large enterprise environment; we applied random forests and multilayer perceptrons to the tasks of browser vs. non-browser identification, browser fingerprinting, and process name prediction. For each of these tasks, we demonstrate how machine learning models can achieve high classification accuracy using only netflow-like features as the basis for classification.