Mapping the Landscape of Generative AI in Network Monitoring and Management
This work addresses the problem of leveraging GenAI for network monitoring and management, which is significant for researchers and practitioners in the field of network management.
This survey explores the application of Generative Artificial Intelligence (GenAI) models in network monitoring and management, highlighting their potential benefits in areas such as network traffic generation and intrusion detection. The survey provides an overview of available GenAI models and datasets, but does not report specific results or numbers.
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.