GGFAST: Automating Generation of Flexible Network Traffic Classifiers
Julien Piet, Dubem Nwoji, Vern Paxson · 2023
When employing supervised machine learning to analyze network traffic, the heart of the task often lies in developing effective features for the ML to leverage. We develop GGFAST, a unified, automated framework that can build powerful classifiers for specific network traffic analysis tasks, built on interpretable features. The framework uses only packet sizes, directionality, and sequencing, facilitating analysis in a payload-agnostic fashion that remains applicable in the presence of encryption.