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

2026-02-06

AI Summary

This work proposes a foundational model for network traffic analysis by introducing the Transformer architecture into this domain for the first time. Addressing the limited generalization and heavy reliance on labeled data in traditional methods, the framework unifies pre-training and multi-task fine-tuning to support diverse downstream tasks such as traffic classification, feature prediction, and generation. By modeling traffic as sequences and leveraging self-supervised pre-training, the model enables effective knowledge transfer across tasks and maintains high performance even under low-label regimes. Experimental results demonstrate that the proposed traffic foundation model significantly outperforms non-foundation baselines across multiple downstream tasks, validating its effectiveness and strong generalization capability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Large Vision Models
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• network traffic analysis
• foundation models
• Transformer
• traffic classification
• limited labeled data

Innovation

• Transformer
• foundation model
• network traffic analysis
• pre-training
• fine-tuning