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. GGFAST analyzes labeled network data to identify n-grams (“snippets”) in a network flow’s sequence-of-message-lengths that are strongly indicative of given categories of activity. The framework then produces a classifier that, given new (unlabeled) network data, identifies the activity to associate with each flow by assessing the presence (or absence) of snippets relevant to the different categories. We demonstrate the power of our framework by building—without any case-specific tuning—highly accurate analyzers for multiple types of network analysis problems. These span traffic classification (L7 protocol identification), finding DNS-over-HTTPS in TLS flows, and identifying specific RDP and SSH authentication methods. Finally, we demonstrate how, given ciphersuite specifics, we can transform a GGFAST analyzer developed for a given type of traffic to automatically detect instances of that activity when tunneled within SSH or TLS.

Julien Piet is a 3rd year Ph.D. student in the EECS department at UC Berkeley, advised by Professors Vern Paxson and David Wagner. He is currently focused on developping new methods to measure network activity and detect specific behaviors.