Mapping the Landscape of Generative AI in Network Monitoring and Management

IF 4.7 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Network and Service Management Pub Date : 2025-02-17 DOI:10.1109/TNSM.2025.3543022

Giampaolo Bovenzi;Francesco Cerasuolo;Domenico Ciuonzo;Davide Di Monda;Idio Guarino;Antonio Montieri;Valerio Persico;Antonio Pescapé

{"title":"Mapping the Landscape of Generative AI in Network Monitoring and Management","authors":"Giampaolo Bovenzi;Francesco Cerasuolo;Domenico Ciuonzo;Davide Di Monda;Idio Guarino;Antonio Montieri;Valerio Persico;Antonio Pescapé","doi":"10.1109/TNSM.2025.3543022","DOIUrl":null,"url":null,"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.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 3","pages":"2441-2472"},"PeriodicalIF":4.7000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network and Service Management","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10891637/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

生成式人工智能在网络监控和管理中的应用

生成式人工智能（GenAI）模型，如llm、gpt和扩散模型，近年来受到了学术界和工业界的广泛关注。本调查探讨了它们在网络监控和管理中的应用，重点关注突出的用例，以及挑战和机遇。我们讨论了网络流量生成和分类、网络入侵检测、网络系统日志分析和网络数字辅助如何从GenAI模型的使用中受益。此外，我们还概述了可用的GenAI模型、大规模训练阶段的数据集以及开发此类模型的平台。最后，我们讨论了可能缓解采用GenAI进行网络监控和管理的障碍的研究方向。我们的调查旨在绘制当前的景观，并为利用GenAI进行网络监测和管理的未来研究铺平道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Network and Service Management Computer Science-Computer Networks and Communications

CiteScore

9.30

自引率

15.10%

发文量

325

期刊介绍： IEEE Transactions on Network and Service Management will publish (online only) peerreviewed archival quality papers that advance the state-of-the-art and practical applications of network and service management. Theoretical research contributions (presenting new concepts and techniques) and applied contributions (reporting on experiences and experiments with actual systems) will be encouraged. These transactions will focus on the key technical issues related to: Management Models, Architectures and Frameworks; Service Provisioning, Reliability and Quality Assurance; Management Functions; Enabling Technologies; Information and Communication Models; Policies; Applications and Case Studies; Emerging Technologies and Standards.