综述生成式AI在网络监控与管理中的应用现状与挑战
Mapping the Landscape of Generative AI in Network Monitoring and Management
- 系统梳理生成式AI在流量生成、入侵检测等场景的应用思路
- 总结现有模型、数据集及开发平台，揭示技术生态全貌
- 适合关注AI赋能网络运维的研究者与工程人员
生成式人工智能（GenAI）模型如大语言模型（LLMs）、GPTs和扩散模型近年来受到研究界与工业界的广泛关注。本综述探讨了其在网络监控与管理中的应用，重点分析了典型应用场景、面临的挑战与机遇。我们讨论了生成式AI在网络流量生成与分类、网络入侵检测、网络系统日志分析以及网络数字助手等方面带来的潜在收益。此外，本文还概述了可用的GenAI模型、大规模训练阶段的数据集，以及相关开发平台。最后，我们探讨了可能缓解生成式AI在该领域落地障碍的研究方向。本研究旨在描绘当前技术图景，为未来利用生成式AI进行网络监控与管理的研究提供指引。
原文摘要 · Abstract (English)
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.
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