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Network data analytics are now at the core of almost every networkingsolution. Nonetheless, limited access to networking data has been an enduringchallenge due to many reasons including complexity of modern networks,commercial sensitivity, privacy and regulatory constraints. In this work, weexplore how to leverage recent advancements in Diffusion Models (DM) togenerate synthetic network traffic data. We develop an end-to-end framework -NetDiffus that first converts one-dimensional time-series network traffic intotwo-dimensional images, and then synthesizes representative images for theoriginal data. We demonstrate that NetDiffus outperforms the state-of-the-arttraffic generation methods based on Generative Adversarial Networks (GANs) byproviding 66.4% increase in fidelity of the generated data and 18.1% increasein downstream machine learning tasks. We evaluate NetDiffus on seven diversetraffic traces and show that utilizing synthetic data significantly improvestraffic fingerprinting, anomaly detection and traffic classification.

NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging