[2507.01976] A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning

Computer Science > Networking and Internet Architecture arXiv:2507.01976 (cs) [Submitted on 23 Jun 2025 (v1), last revised 8 Apr 2026 (this version, v2)] Title:A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning Authors:Nirhoshan Sivaroopan, Kaushitha Silva, Chamara Madarasingha, Thilini Dahanayaka, Guillaume Jourjon, Anura Jayasumana, Kanchana Thilakarathna View a PDF of the paper titled A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning, by Nirhoshan Sivaroopan and 6 other authors View PDF HTML (experimental) Abstract: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 app...