A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
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Abstract (arXiv)
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. With the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML), we focus particularly on deep learning (DL)-based techniques while also providing a detailed discussion of statistical methods and their extensions, including commercially available tools. We present a comprehensive comparision of generation approaches and provide an AI tool to apply this comparision for any network traffic generation papers. Furthermore, we highlight open challenges in this domain and discuss potential future directions for further research and development. This survey serves as a foundational resource for researchers and practitioners, offering a structured analysis of existing methods, challenges, and opportunities in synthetic network traffic generation.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 47 of its 209 references that are in the map. First posted on arXiv 23 Jun 2025.
Builds on
47 of 209 references.
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.