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The repository of TrafficLLM, a universal LLM adaptation framework to learn robust traffic representation for all open-sourced LLM in real-world scenarios and enhance the generalization across diverse traffic analysis tasks.
| Date | Stars |
|---|---|
| 2026-07-31 | 451 |
| 2026-08-05 | 453 |
| 2026-08-06 | 453 |
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# TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Robust Traffic Representation <p align="center"> <a href='https://github.com/ZGC-LLM-Safety/TrafficLLM'><img src='https://img.shields.io/badge/Project-Github-purple'></a> <a href='https://arxiv.org/abs/2504.04222'><img src='https://img.shields.io/badge/Paper-Arxiv-orange'></a> <a href='https://drive.google.com/drive/folders/1RZAOPcNKq73-quA8KG_lkAo_EqlwhlQb'><img src='https://img.shields.io/badge/Datasets-Google Drive-red'></a> <a href='https://drive.google.com/drive/folders/1YjEhdordqZRpnw_oKczwUztcT52T0oQ0'><img src='https://img.shields.io/badge/Models-Google Drive-green'></a> <a href='https://mp.weixin.qq.com/s/pt2CfG0i9Fex-sy7-dcoyg' target='_blank'><img src='https://img.shields.io/badge/Chinese-Blog-blue'></a> </p> The repository of **TrafficLLM**, a universal LLM adaptation framework to learn robust traffic representation for all open-sourced LLM in real-world scenarios and enhance the generalization across diverse traffic analysis tasks.  Note: this code is based on [ChatGLM2](https://github.com/THUDM/ChatGLM2-6B) and [Llama2](https://github.com/meta-llama/llama-recipes). Many thanks to the authors. ## News - [x] [2025.11.06] 🌟🌟 We update the [MCP configuration](https://github.com/ZGC-LLM-Safety/TrafficLLM/tree/master/agent) that uses TrafficLLM to build single-agent and multi-agent systems for network traffic analysis. - [x] [2025.04.05] 🔥🔥 We release the preprint paper at the [Arxiv website](https://arxiv.org/abs/2504.04222). - [x] [2024.11.26] 🌲🌲 We release the generation code to use TrafficLLM to generate packets with [Scapy](https://scapy.net/), which can generate pcap files that can be read by Wireshark. Go to [tutorials](https://github.com/ZGC-LLM-Safety/TrafficLLM/tree/master/tutorials) for more details. - [x] [2024.10.28] 🎉🎉 We have update the adaptation code for using GLM4 to build TrafficLLM, which has a faster tuning and inference speed than ChatGLM2. Go to [Adapt2GLM4](https://github.com/ZGC-LLM-Safety/TrafficLLM/tree/master/Adapt2GLM4) for more details. ## Brief Introduction **TrafficLLM** is built upon a sophisticated fine-tuning framework using natural language and traffic data, which proposes the following techniques to enhance the utility of large language models in network traffic analysis. * **Traffic-Domain Tokenization.** To overcome the modality gap between natural language and heterogeneous traffic data, TrafficLLM introduces traffic-domain tokenization to process the diverse input of traffic detection and generation tasks for LLM adaptation. This mechanism effectively extends LLM’s native tokenizer by training specialized the tokenization model on large-scale traffic-domain corpora. * **Dual-Stage Tuning Pipeline.** TrafficLLM employs a dual-stage tuning pipeline to achieve LLM’s robust representation learning across different traffic-domain tasks. The pipeline trains LLM to understand instructions and learn task-related traffic patterns at different stages, which builds upon TrafficLLM task understanding and traffic reasoning abilities for diverse traffic detection and generation tasks. * **Extensible Adaptation with Parameter-Effective Fine-Tuning (EA-PEFT).** To adapt LLM for generalization to new traffic environments, TrafficLLM proposes an extensible adaptation with parameter-effective fine-tuning (EA-PEFT) to update model parameters with low overhead. The technique splits model capabilities in different PEFT models, which helps minimize the costs on dynamic scenarios raised by traffic pattern changes. ## TrafficLLM Datasets We released TrafficLLM's [training datasets](https://drive.google.com/drive/folders/1RZAOPcNKq73-quA8KG_lkAo_EqlwhlQb), which contains over 0.4M traffic data and 9K human instructions for LLM adaptation across different traffic analysis tasks. * `Instruction Datasets`: The instruction datasets ar
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:aea03fffe990086b, llm:description: 'TrafficLLM, a universal LLM adaptation framework to learn robust traffic representation... enhance generalization across diverse traffic analysis tasks.'
matched fp:aea03fffe990086b, llm:description: 'TrafficLLM, a universal LLM adaptation framework to learn robust traffic representation... enhance generalization across diverse traffic analysis tasks.'
matched fp:aea03fffe990086b, llm:description: 'TrafficLLM, a universal LLM adaptation framework to learn robust traffic representation... enhance generalization across diverse traffic analysis tasks.'