用自然语言生成可交互的网络仿真环境，让配置更简单。
Text2Net: Transforming Plain-text To A Dynamic Interactive Network Simulation Environment
- 输入文字描述自动构建动态网络仿真
- 部署网络场景耗时比传统工具减少超50%
- 适合学生、教师及网络工程师快速上手
本文提出Text2Net，一种基于自然语言处理与大语言模型的文本驱动网络仿真引擎，能将纯文本描述的网络拓扑自动转化为动态交互式仿真环境。该系统简化了网络仿真的配置流程，无需用户掌握厂商特定语法或复杂图形界面。通过定性与定量评估，Text2Net显著降低了部署网络场景所需的时间与精力，相较EVE-NG等传统仿真器效率更高。通过自动化重复任务和直观交互，提升了学生、教育者及专业人员的使用便利性，助力理论与实践结合的教学体验。结果表明其在多种网络复杂度下均具可扩展性，为网络教育与概念验证测试带来革新。
原文摘要 · Abstract (English)
This paper introduces Text2Net, an innovative text-based network simulation engine that leverages natural language processing (NLP) and large language models (LLMs) to transform plain-text descriptions of network topologies into dynamic, interactive simulations. Text2Net simplifies the process of configuring network simulations, eliminating the need for users to master vendor-specific syntaxes or navigate complex graphical interfaces. Through qualitative and quantitative evaluations, we demonstrate Text2Net's ability to significantly reduce the time and effort required to deploy network scenarios compared to traditional simulators like EVE-NG. By automating repetitive tasks and enabling intuitive interaction, Text2Net enhances accessibility for students, educators, and professionals. The system facilitates hands-on learning experiences for students that bridge the gap between theoretical knowledge and practical application. The results showcase its scalability across various network complexities, marking a significant step toward revolutionizing network education and professional use cases, such as proof-of-concept testing.
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