GGFAST: Automating Generation of Flexible Network Traffic Classifiers
Julien Piet, Dubem Nwoji, Vern Paxson
Abstract
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.
Topics & Concepts
Computer scienceLeverage (statistics)Deep packet inspectionPayload (computing)Traffic classificationArtificial intelligenceEncryptionMachine learningTask (project management)Network packetTraffic analysisData miningComputer networkEngineeringSystems engineeringInternet Traffic Analysis and Secure E-votingNetwork Security and Intrusion DetectionAdvanced Malware Detection Techniques