A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning

ArXi:2507.01976v2 Announce Type: replace-cross Synthetic network traffic generation has emerged as a promising alternative for various data-driven applications in the networking domain. It enables the creation of synthetic data that preserves real-world characteristics while addressing key challenges such as data scarcity, privacy concerns, and purity constraints associated with real data. In this survey, we provide a comprehensive review of synthetic network traffic generation approaches, covering essential aspects such as data types and generation models.