NetDiffus: Network Traffic Generation by Diffusion Models through
  Time-Series Imaging
Nirhoshan Sivaroopan Dumindu Bandara Chamara Madarasingha Guilluame Jourjon Anura Jayasumana Kanchana Thilakarathna
Network data analytics are now at the core of almost every networking
solution. Nonetheless, limited access to networking data has been an enduring
challenge due to many reasons including complexity of modern networks,
commercial sensitivity, privacy and regulatory constraints. In this work, we
explore how to leverage recent advancements in Diffusion Models (DM) to
generate synthetic network traffic data. We develop an end-to-end framework -
NetDiffus that 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 fidelity of the generated data and 18.1% increase
in downstream machine learning tasks. We evaluate NetDiffus on seven diverse
traffic traces and show that utilizing synthetic data significantly improves
traffic fingerprinting, anomaly detection and traffic classification.