Synthetic Traffic Generation

Synthetic traces, primarily emphasizing certain flow statistics or packet attributes, are frequently used to support ML tasks in networking. However, their limited alignment with real traces and challenges in converting to raw network traffic hinder both their ML performance and broader applicability in conventional network analyses. In our research, we tap into the promising capabilities of generative models to enhance synthetic network traffic production.

NetSSM: Multi-Flow and State-Aware Network Trace Generation using State-Space Models

Abstract. Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high collection costs and governance rules. Previous efforts explore this challenge by generating synthetic network data, but fail to reliably han- dle multi-ow sessions, struggle to reason about stateful communication in moderate to long-duration net- work sessions, and lack robust evaluations tied to real-world utility. We propose a new method based on state-space models called NetSSM that generates raw network traffic at the packet-level granularity. Our approach captures interactions between multiple, interleaved ows – an objective unexplored in prior work – and effectively reasons about ow-state in sessions to capture traffic characteristics. NetSSM accomplishes this by training with a context window more than 8× longer, and produces traces up to 78× longer than exist- ing transformer-based raw packet generators. Evaluation results show that NetSSM generates high-fidelity traces that outperform prior efforts in existing benchmarks. We also nd that NetSSM’s traces have high semantic similarity to real network data regarding compliance with standard protocol requirements and ow and session-level traffic characteristics.

Resources

Source code and results: https://github.com/noise-lab/netssm

Citation bibtex

@article{chu2026netssm,
  title={NetSSM: Multi-Flow and State-Aware Network Trace Generation using State-Space Models},
  author={Chu, Andrew and Jiang, Xi and Liu, Shinan and Nitin Bhagoji, Arjun  and Bronzino, Francesco and Schmitt, Paul and Feamster, Nick},
  journal={Proceedings of the ACM on Networking},
  volume={1},
  number={CoNEXT5},
  year={2026}
}

NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation

Abstract. 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 NetDiffusion, 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.

Resources

Source code and results: https://github.com/noise-lab/NetDiffusion_Generator

Citation bibtex

@article{xi2024netdiffusion,
  title={NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation},
  author={Jiang, Xi and Liu, Shinan and Gember-Jacobson, Aaron and Nitin Bhagoji, Arjun  and Schmitt, Paul and Bronzino, Francesco and Feamster, Nick},
  journal={Proceedings of the ACM on Measurement and Analysis of Computing Systems},
  year={2024}
}