Text2Net: Transforming Plain-text To A Dynamic Interactive Network Simulation Environment

TL;DR

Text2Net addresses the barriers in network simulation education caused by vendor-specific syntaxes and complex interfaces by translating plain-English topology descriptions into executable configurations via NLP/LLMs. It introduces a five-module pipeline that yields Structured Command Strings and a JSON topology, which are then provisioned into EVE-NG, enabling rapid, interactive network experiments. The approach demonstrates substantial time and effort reductions across three escalating scenarios and receives strong educational usability feedback, indicating broad potential for both classrooms and professional prototyping. By validating the concept as a scalable, AI-assisted path to practical network learning and testing, Text2Net advances accessible, hands-on experimentation in networking education and beyond.

Abstract

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.

Figures (9)

• Figure 1: Text2Net system model and pipeline
• Figure 2: System flow perspectives.
• Figure 3: Static-route Scenario Network Topology
• Figure 4: Structured Command Strings (SCSs)
• Figure 5: key-value pairs output