Understanding the traffic dynamics in networks is a core capability for automated systems to monitor and analyze networking behaviors, reducing expensive human efforts and economic risks through tasks such as traffic classification, congestion prediction, and attack detection. However, it is still challenging to accurately model network traffic with machine learning approaches in an efficient and broadly applicable manner. Task-specific models trained from scratch are used for different networking applications, which limits the efficiency of model development and generalization of model deployment. Furthermore, while networking data is abundant, high-quality task-specific labels are often insufficient for training individual models. Large-scale self-supervised learning on unlabeled data provides a natural pathway for tackling these challenges. We propose to pre-train a general-purpose machine learning model to capture traffic dynamics with only traffic data from NetFlow records, with the goal of fine-tuning for different downstream tasks with small amount of labels. Our presented NetFlowGen framework goes beyond a proof-of-concept for network traffic pre-training and addresses specific challenges such as unifying network feature representations, learning from large unlabeled traffic data volume, and testing on real downstream tasks in DDoS attack detection. Experiments demonstrate promising results of our pre-training framework on capturing traffic dynamics and adapting to different networking tasks.

本研究解决了网络流量建模准确性差和缺乏高质量标签的问题，提出了一种通用的机器学习预训练模型NetFlowGen，通过使用NetFlow记录中的流量数据自我监督学习，从而提升了模型的开发效率和应用广泛性。实验结果表明，该框架在捕捉流量动态和适应不同网络任务方面具有良好的效果，尤其在DDoS攻击检测中展现了潜在的应用价值。

NetFlowGen：利用生成预训练模型分析网络流量动态