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

TL;DR

Text2Net is a novel NLP-powered network simulation engine that converts plain-text descriptions into interactive network environments, simplifying setup and enhancing accessibility for education and professional testing.

Contribution

It introduces a new approach that uses large language models to automate network configuration from plain text, reducing complexity and setup time compared to traditional tools.

Findings

Significantly reduces deployment time for network scenarios.

Enhances accessibility for educational and professional use.

Scales effectively across various network complexities.

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.