Predicting Process Name from Network Data

Justin Allen; David Knapp; Kristine Monteith

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arXiv · 2109.03328

Predicting Process Name from Network Data

Justin Allen·David Knapp·Kristine Monteith

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Abstract

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.

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BibTeX RIS

Justin Allen, David Knapp, Kristine Monteith. 2021-09-03. Predicting Process Name from Network Data. https://arxiv.org/abs/2109.03328

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