NetDiffus: Network traffic generation by diffusion models through time-series imaging

Abstract

While Machine-Learning based network data analytics are now commonplace for many networking solutions, nonetheless, limited access to appropriate networking data has been an enduring challenge for many networking problems. Causes for lack of such data include complexity of data gathering, commercial sensitivity, as well as privacy and regulatory constraints. To overcome these challenges, we present a Diffusion-Model (DM) based end-to-end framework, NetDiffus, for synthetic network traffic generation which is one of the emerging topics in networking and computing system. NetDiffus first converts one-dimensional time-series network traffic into two-dimensional images, and then synthesizes representative images for the original data. We demonstrate that NetDiffus outperforms the state-of-the-art traffic generation methods based on Generative Adversarial Networks (GANs) by providing 66.4% increase in the fidelity of the generated data and an 18.1% increase in downstream machine learning tasks. We evaluate NetDiffus on seven diverse traffic traces and show that utilizing synthetic data significantly improves several downstream ML tasks including traffic fingerprinting, anomaly detection and traffic classification. The code has been included at https://github.com/Nirhoshan/NetDiffus.