综述网络流量生成技术，从统计模型到深度学习方法。
A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
- 系统梳理统计模型与深度学习的流量生成方法。
- 对比多种生成技术并提供可复用的AI评估工具。
- 适合网络研究者和安全领域从业者参考。
合成网络流量生成已成为网络领域数据驱动应用的重要替代方案。它能生成保留真实特征的合成数据，有效应对真实数据存在的数据稀缺、隐私问题和纯净性约束。本文全面回顾了合成网络流量生成方法，涵盖数据类型与生成模型。随着人工智能（AI）与机器学习（ML）的快速发展，重点分析了深度学习（DL）方法，同时详述了统计方法及其扩展，包括商用工具。我们提供了生成方法的综合对比，并开发了一个AI工具，可对任意网络流量生成论文进行评估。此外，文章指出了该领域的开放挑战，并探讨了未来研究方向。本综述为研究人员与实践者提供了结构化分析，涵盖现有方法、挑战与机遇。
原文摘要 · Abstract (English)
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.
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