# NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation

arXiv: https://arxiv.org/abs/2310.08543 · HTML: https://mlsanity.com/paper/2310.08543/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2023.

## Built on by (most cited first, 27 of 27)

- [Lens: A Knowledge-Guided Foundation Model for Network Traffic](https://mlsanity.com/paper/2402.03646/) (2024)
- [Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study](https://mlsanity.com/paper/2411.08341/) (2024)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection](https://mlsanity.com/paper/2503.03022/) (2025)
- [Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic](https://mlsanity.com/paper/2511.20497/) (2025)
- [Making Logic a First-Class Citizen in Generative ML for Networking](https://mlsanity.com/paper/2506.23964/) (2025)
- [A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning](https://mlsanity.com/paper/2507.01976/) (2025)
- [Demystifying Network Foundation Models](https://mlsanity.com/paper/2509.23089/) (2025)
- [PLUME: Building a Network-Native Foundation Model for Wireless Traces via Protocol-Aware Tokenization](https://mlsanity.com/paper/2603.13647/) (2026)
- [TabQueryBench: A Query-Centric Benchmark for Synthetic Tabular Data](https://mlsanity.com/paper/2607.03926/) (2026)
- [FlowWise: Stateful Fast-Slow Model Serving for Streaming Traffic Intelligence](https://mlsanity.com/paper/2402.03694/) (2024)
- [Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic](https://mlsanity.com/paper/2408.16034/) (2024)
- [MYCROFT: Towards Effective and Efficient External Data Augmentation](https://mlsanity.com/paper/2410.08432/) (2024)
- [UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Security](https://mlsanity.com/paper/2503.04174/) (2025)
- [PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation](https://mlsanity.com/paper/2602.08331/) (2026)
- [A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation](https://mlsanity.com/paper/2604.21623/) (2026)
- [SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing](https://mlsanity.com/paper/2410.06112/) (2024)
- [The Phantom of PCIe: Constraining Generative Artificial Intelligences for Practical Peripherals Trace Synthesizing](https://mlsanity.com/paper/2411.06376/) (2024)
- [Taming Data Challenges in ML-based Security Tasks Using Generative AI](https://mlsanity.com/paper/2507.06092/) (2025)
- [Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion](https://mlsanity.com/paper/2507.20115/) (2025)
- [Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurement](https://mlsanity.com/paper/2509.19669/) (2025)
- [Denoising Refinement Diffusion Models for Simultaneous Generation of Multi-scale Mobile Network Traffic](https://mlsanity.com/paper/2511.17532/) (2025)
- [Lightweight GenAI for Network Traffic Generation: Fidelity, Augmentation, and Classification](https://mlsanity.com/paper/2603.25507/) (2026)
- [TraceCodec: A Compiler-Backed Neural Codec for Network Traffic Traces](https://mlsanity.com/paper/2605.29941/) (2026)
- [One-Prompt Censorship Evasion via Generative Diffusion Models](https://mlsanity.com/paper/2606.22717/) (2026)
- [Invariant Discovery for Networked Systems](https://mlsanity.com/paper/2607.22944/) (2026)
- [ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models](https://mlsanity.com/paper/2608.10120/) (2026)

## Abstract (arXiv)

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.