Mapping the Landscape of Generative AI in Network Monitoring and Management
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 79 of its 195 references that are in the map. First posted on arXiv 12 Feb 2025.
Builds on
79 of 195 references.
References · 79
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
- 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2016Density estimation using Real NVPcited by 2,042
- 2014NICE: Non-linear Independent Components Estimationcited by 1,058
- 2019Green AIcited by 627
38 more
- 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 411
- 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
- 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
- 2023Understanding Diffusion Objectives as the ELBO with Simple Data Augmentationcited by 304
- 2024LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuningcited by 136
- 2022Causal Machine Learning: A Survey and Open Problemscited by 110
- 2023Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantizationcited by 100
- 2018NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating Systemcited by 98
- 2017Quantum machine learning: a classical perspectivecited by 95
- 2015Imaging Time-Series to Improve Classification and Imputationcited by 85
- 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
- 2023Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Servicescited by 76
- 2020Loghub: A Large Collection of System Log Datasets for AI-driven Log Analyticscited by 70
- 2022ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classificationcited by 66
- 2023TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledgecited by 63
- 2023Large Language Models Empowered Autonomous Edge AI for Connected Intelligencecited by 56
- 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
- 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
- 2020A Survey on Automated Log Analysis for Reliability Engineeringcited by 50
- 2018Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learningcited by 43
- 2023Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devicescited by 39
- 2023LogGPT: Exploring ChatGPT for Log-Based Anomaly Detectioncited by 35
- 2024A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directionscited by 33
- 2023LILAC: Log Parsing using LLMs with Adaptive Parsing Cachecited by 32
- 2023NetGPT: Generative Pretrained Transformer for Network Trafficcited by 31
- 2016IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoTcited by 30
- 2020Technology Readiness Levels for AI & MLcited by 30
- 2023What do LLMs need to Synthesize Correct Router Configurations?cited by 29
- 2023Large Language Models for Networking: Applications, Enabling Techniques, and Challengescited by 28
- 2023NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generationcited by 27
- 2020New Directions in Automated Traffic Analysiscited by 26
- 2023Federated Learning-Empowered AI-Generated Content in Wireless Networkscited by 26
- 2023LogGPT: Log Anomaly Detection via GPTcited by 26
- 2023HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)cited by 25
- 2024At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligencecited by 25
- 2023FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systemscited by 24
- 2024NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mambacited by 24
50 most cited of 79 All 79 (JSON)
Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Geminicited by 0
- 2025LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Costcited by 0
- 2026Lightweight GenAI for Network Traffic Generation: Fidelity, Augmentation, and Classificationcited by 0
- 2026A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocationcited by 0
- 2026A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation24 Jul 2026
- 2026Lightweight GenAI for Network Traffic Generation: Fidelity, Augmentation, and Classification26 Mar 2026
- 2025LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Cost11 Nov 2025
- 2025From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini13 Oct 2025
Similar, not cited
Close in wording, no citation either way
- 2024Generative AI in Data Center Networking: Fundamentals, Perspectives, and Case Study
- 2024Review of Generative AI Methods in Cybersecurity
- 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Survey
- 2025Generative AI for Software Architecture. Applications, Challenges, and Future Directions
- 2023Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study
- 2024A Primer on Generative AI for Telecom: From Theory to Practice
- 2025Generative AI in Transportation Planning: A Survey
- 2023Identifying and Mitigating the Security Risks of Generative AI