Talk Like a Packet: Rethinking Network Traffic Analysis with Transformer Foundation Models

Abstract

Inspired by the success of Transformer-based models in natural language processing, this paper investigates their potential as foundation models for network traffic analysis. We propose a unified pre-training and fine-tuning pipeline for traffic foundation models. Through fine-tuning, we demonstrate the generalizability of the traffic foundation models in various downstream tasks, including traffic classification, traffic characteristic prediction, and traffic generation. We also compare against non-foundation baselines, demonstrating that the foundation-model backbones achieve improved performance. Moreover, we categorize existing models based on their architecture, input modality, and pre-training strategy. Our findings show that these models can effectively learn traffic representations and perform well with limited labeled datasets, highlighting their potential in future intelligent network analysis systems.