GPT on the wire: Towards realistic network traffic conversations generated with large language models

Abstract

Realistic network traffic generation is essential for evaluating the performance, security, and scalability of modern communication systems. Traditional methods, such as traffic replay systems and statistical models, while useful, often fall short in capturing the complexity and variability of real-world network scenarios. Recent advancements in Artificial Intelligence (AI), especially Large Language Models (LLMs) like ChatGPT, have introduced new approaches to synthetic traffic generation. This paper presents a novel architecture using OpenAI’s GPT-3.5 Turbo to generate synthetic network traffic, with a focus on creating multi-protocol conversations that are indistinguishable from real-world interactions. Through fine-tuning and prompt engineering, the proposed system successfully generates packet- and conversation-level network traffic for ICMP, ARP, DNS, TCP and HTTP protocols. Additionally, by integrating a Mixture of Experts (MoE) architecture, this model simulates real-world network conversations with high accuracy, being able to generate a conversation combining ARP, DNS, TCP and HTTP without packet or protocol errors. The results show how the application of LLMs in network traffic generation improves realism and adaptability, establishing this approach as a valuable tool for future security testing and network performance evaluation. In addition, the proposed methodology is easily adaptable to other LLMs available both through APIs and to be downloaded and executed on your own computer.