用自然语言定义网络应用流程，自动生成真实流量数据
NetGent: Agent-Based Automation of Network Application Workflows
- 通过自然语言规则生成状态机，实现流程自动化
- 覆盖50+种应用流程，生成的流量真实且可复现
- 适合需要大量真实网络数据的研究者使用
我们提出NetGent，一个基于AI代理的框架，用于自动化复杂应用工作流，生成逼真的网络流量数据集。构建通用的网络机器学习模型需要从包含多种真实网络应用流量的环境中采集数据，但现有浏览器自动化工具在多样性、可重复性、真实性和效率方面仍存在脆弱和成本高的问题。NetGent通过允许用户以自然语言规则描述依赖状态的动作来解决这一挑战，这些抽象规范被编译为非确定性有限自动机（NFAs），再由状态合成组件转换为可重用、可执行的代码。该设计实现了确定性回放，通过状态缓存减少冗余LLM调用，并在应用界面变化时快速适应。实验表明，NetGent成功自动化了超过50个工作流，涵盖视频点播、直播视频、视频会议、社交媒体和网页抓取，生成了真实流量痕迹，且对UI变化具有鲁棒性。结合语言代理的灵活性与编译执行的可靠性，NetGent为推进网络领域机器学习提供了可扩展的数据生成基础。
原文摘要 · Abstract (English)
We present NetGent, an AI-agent framework for automating complex application workflows to generate realistic network traffic datasets. Developing generalizable ML models for networking requires data collection from network environments with traffic that results from a diverse set of real-world web applications. However, using existing browser automation tools that are diverse, repeatable, realistic, and efficient remains fragile and costly. NetGent addresses this challenge by allowing users to specify workflows as natural-language rules that define state-dependent actions. These abstract specifications are compiled into nondeterministic finite automata (NFAs), which a state synthesis component translates into reusable, executable code. This design enables deterministic replay, reduces redundant LLM calls through state caching, and adapts quickly when application interfaces change. In experiments, NetGent automated more than 50+ workflows spanning video-on-demand streaming, live video streaming, video conferencing, social media, and web scraping, producing realistic traffic traces while remaining robust to UI variability. By combining the flexibility of language-based agents with the reliability of compiled execution, NetGent provides a scalable foundation for generating the diverse, repeatable datasets needed to advance ML in networking.
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