Mapping the Landscape of Generative AI in Network Monitoring and Management

TL;DR

This survey reviews how Generative AI models like LLMs and diffusion models are applied in network monitoring and management, highlighting current use cases, challenges, and future research directions.

Contribution

It provides a comprehensive overview of GenAI applications in network management, including models, datasets, platforms, and research challenges, mapping the current landscape and future opportunities.

Findings

GenAI models enhance network traffic analysis and intrusion detection.

Available datasets and platforms support large-scale GenAI training for networks.

Research directions aim to overcome adoption challenges in network contexts.

Abstract

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.