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

TL;DR

This survey comprehensively reviews methods for synthetic network traffic generation, emphasizing AI and deep learning techniques, and discusses challenges and future research directions in the field.

Contribution

It provides a detailed comparison of statistical and deep learning approaches, including an AI tool for evaluating network traffic generation methods.

Findings

Deep learning methods are increasingly used for traffic synthesis.

Commercial tools incorporate advanced statistical and AI models.

Open challenges include data privacy and realistic traffic modeling.

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 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.