Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks. Effective pretrained models can significantly optimize the training efficiency and effectiveness of downstream tasks, such as traffic classification, attack detection, resource scheduling, protocol analysis, and traffic generation. Despite the great success of pretraining in natural language processing, there is no work in the network field. Considering the diverse demands and characteristics of network traffic and network tasks, it is non-trivial to build a pretrained model for network traffic and we face various challenges, especially the heterogeneous headers and payloads in the multi-pattern network traffic and the different dependencies for contexts of diverse downstream network tasks. To tackle these challenges, in this paper, we make the first attempt to provide a generative pretrained model for both traffic understanding and generation tasks. We propose the multi-pattern network traffic modeling to construct unified text inputs and support both traffic understanding and generation tasks. We further optimize the adaptation effect of the pretrained model to diversified tasks by shuffling header fields, segmenting packets in flows, and incorporating diverse task labels with prompts. Expensive experiments demonstrate the effectiveness of our NetGPT in a range of traffic understanding and generation tasks, and outperform state-of-the-art baselines by a wide margin.

翻译：网络流量的预训练模型能够利用大规模原始数据学习网络流量的本质特征，并在无需考虑特定下游任务的情况下为输入流量生成可区分的结果。有效的预训练模型可显著优化流量分类、攻击检测、资源调度、协议分析和流量生成等下游任务的训练效率与效果。尽管预训练在自然语言处理领域取得了巨大成功，但在网络领域尚无相关工作。考虑到网络流量及网络任务的多样化需求与特性，构建面向网络流量的预训练模型面临诸多挑战，尤其是多模式网络流量中异构的头部与载荷，以及多样化下游网络任务中不同的上下文依赖关系。为应对这些挑战，本文首次尝试提出一种面向流量理解与流量生成任务的生成式预训练模型。我们提出多模式网络流量建模方法，构建统一文本输入以同时支持流量理解与生成任务。通过调整头部字段顺序、对数据流中的数据包进行分段分割，以及将多样化任务标签与提示词相结合，进一步优化预训练模型对多样化任务的适配效果。大量实验表明，我们的NetGPT在多种流量理解与生成任务中具有有效性，并以显著优势超越现有最优基线方法。