Year: 2025
Type: conference-paper
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.
Source: Proceedings of IEEE Southeastcon
Authors: Alireza Marefat, Abbaas Alif Mohamed Nishar, Ashwin Ashok
Institution: Georgia State University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W4409796809