NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics

2024-12-30

AI Summary

To address key bottlenecks in network traffic analysis—namely poor few-shot adaptability, strong label dependency, and weak cross-domain generalization—this paper proposes the first generative pre-training framework specifically designed for NetFlow data. Our method employs a Transformer-based architecture with a self-supervised generative pre-training objective, enabling unified representation learning and large-scale unsupervised modeling of network flow features. A lightweight fine-tuning mechanism is introduced to rapidly adapt the pre-trained model to diverse downstream tasks, including classification, congestion prediction, and DDoS detection. Evaluated on real-world DDoS detection, the approach achieves 92.4% accuracy using only limited labeled data—outperforming supervised baselines by 12.7%. Moreover, the pre-trained model demonstrates strong transferability across heterogeneous network environments. This work establishes the first unified pre-training paradigm for NetFlow representation learning, advancing foundational methodology for traffic analytics.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Machine Learning
• Network Data Analysis
• Model Adaptability

Innovation

• Generative Pre-training
• Self-supervised Learning
• NetFlow Analysis