NetDiffus：通过时序成像利用扩散模型生成网络流量
NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging
AI总结：
针对网络数据获取受限的问题，提出端到端框架NetDiffus，将一维时序流量转二维图像后用扩散模型生成合成数据，在7类流量轨迹上验证其性能优于GAN类方法，可提升多项下游网络任务效果。
摘要：
网络数据分析如今几乎是所有网络解决方案的核心。然而，由于现代网络的复杂性、商业敏感性、隐私及监管限制等诸多原因，网络数据的获取受限一直是个长期存在的挑战。本研究探索如何利用扩散模型（Diffusion Models，DM）的最新进展生成合成网络流量数据。我们开发了一个端到端框架——NetDiffus，该框架首先将一维时序网络流量转换为二维图像，再合成原始数据的代表性图像。实验证明，NetDiffus的性能优于基于生成对抗网络（Generative Adversarial Networks，GANs）的当前最优流量生成方法，生成数据的保真度提升66.4%，在下游机器学习任务中的表现提升18.1%。我们在7种不同的流量轨迹上评估了NetDiffus，结果表明使用合成数据可显著改善流量指纹识别、异常检测和流量分类的效果。
英文摘要：
Network data analytics are now at the core of almost every networking solution. Nonetheless, limited access to networking data has been an enduring challenge due to many reasons including complexity of modern networks, commercial sensitivity, privacy and regulatory constraints. In this work, we explore how to leverage recent advancements in Diffusion Models (DM) to generate synthetic network traffic data. We develop an end-to-end framework - NetDiffus that first converts one-dimensional time-series network traffic into two-dimensional images, and then synthesizes representative images for the original data. We demonstrate that NetDiffus outperforms the state-of-the-art traffic generation methods based on Generative Adversarial Networks (GANs) by providing 66.4% increase in fidelity of the generated data and 18.1% increase in downstream machine learning tasks. We evaluate NetDiffus on seven diverse traffic traces and show that utilizing synthetic data significantly improves traffic fingerprinting, anomaly detection and traffic classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
