Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concerns, such as data staleness. To overcome this limitation, synthetic network traces can often augment existing datasets. Unfortunately, current synthetic trace generation methods, which typically produce only aggregated flow statistics or a few selected packet attributes, do not always suffice, especially when model training relies on having features that are only available from packet traces. This shortfall manifests in both insufficient statistical resemblance to real traces and suboptimal performance on ML tasks when employed for data augmentation. In this paper, we apply diffusion models to generate high-resolution synthetic network traffic traces. We present NetDiffusion1, a tool that uses a finely-tuned, controlled variant of a Stable Diffusion model to generate synthetic network traffic that is high fidelity and conforms to protocol specifications. Our evaluation demonstrates that packet captures generated from NetDiffusion can achieve higher statistical similarity to real data and improved ML model performance than current state-of-the-art approaches (e.g., GAN-based approaches). Furthermore, our synthetic traces are compatible with common network analysis tools and support a myriad of network tasks, suggesting that NetDiffusion can serve a broader spectrum of network analysis and testing tasks, extending beyond ML-centric applications.

Xi Jiang

Shinan Liu

Aaron Gember-Jacobson

Arjun Nitin Bhagoji

Paul Schmitt

Francesco Bronzino

Nick Feamster

Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proc. ACM Meas. Anal. Comput. Syst.

NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation

NetDiffusion：通过协议约束的流量生成实现网络数据增强

NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	3