Mapping the Landscape of Generative AI in Network Monitoring and Management | Paper Citation Record

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This paper cites Harnessing the power of llms in practice: A survey on chatgpt and beyond,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Harnessing the power of llms in practice: A survey on chatgpt and beyond,

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This paper cites Generative Artificial Intelligence (AI) Market Size Worldwide from 2020 to 2030,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Generative Artificial Intelligence (AI) Market Size Worldwide from 2020 to 2030,

Reference 2

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This paper cites When Digital Twin Meets Generative AI: Intelligent Closed-Loop Network Management.

Mapping the Landscape of Generative AI in Network Monitoring and Management When Digital Twin Meets Generative AI: Intelligent Closed-Loop Network Management

Reference 3

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This paper cites The Age of Generative AI and AI-generated Everything,.

Mapping the Landscape of Generative AI in Network Monitoring and Management The Age of Generative AI and AI-generated Everything,

Reference 4

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This paper cites Technology readiness levels for machine learning systems,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Technology readiness levels for machine learning systems,

Reference 5

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This paper cites Landing AI on Networks: An Equipment Ven- dor Viewpoint on Autonomous Driving Networks,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Landing AI on Networks: An Equipment Ven- dor Viewpoint on Autonomous Driving Networks,

Reference 6

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This paper cites The Networking Channel,.

Mapping the Landscape of Generative AI in Network Monitoring and Management The Networking Channel,

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This paper cites AT&T’s new Generative AI Tool Will Help Employees Be More Effective, Creative, and Innovative,.

Mapping the Landscape of Generative AI in Network Monitoring and Management AT&T’s new Generative AI Tool Will Help Employees Be More Effective, Creative, and Innovative,

Reference 8

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This paper cites Cisco Artificial Intelligence,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Cisco Artificial Intelligence,

Reference 9

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This paper cites How to Make Better Use of Network Insights with Gen- erative AI,.

Mapping the Landscape of Generative AI in Network Monitoring and Management How to Make Better Use of Network Insights with Gen- erative AI,

Reference 10

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This paper cites European Innovation Council,.

Mapping the Landscape of Generative AI in Network Monitoring and Management European Innovation Council,

Reference 11

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This paper cites Huawei Introduces AI Technologies to Accelerate Network Trans- formation Towards All Intelligence in the Net5.5G Era,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Huawei Introduces AI Technologies to Accelerate Network Trans- formation Towards All Intelligence in the Net5.5G Era,

Reference 12

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This paper cites Welcome to the Large Generative Al Models inTelecom (GenAlNet) Emerging TechnologyInitiative website,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Welcome to the Large Generative Al Models inTelecom (GenAlNet) Emerging TechnologyInitiative website,

Reference 13

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This paper cites IETF Side Meetings,.

Mapping the Landscape of Generative AI in Network Monitoring and Management IETF Side Meetings,

Reference 14

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This paper cites Specializing Large Language Models for Telecom Networks,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Specializing Large Language Models for Telecom Networks,

Reference 15

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This paper cites Generative AI implications for Telco Operations,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Generative AI implications for Telco Operations,

Reference 16

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This paper cites Telefónica Partners with Microsoft to Incorporate Generative AI into Kernel,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Telefónica Partners with Microsoft to Incorporate Generative AI into Kernel,

Reference 17

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This paper cites Generative AI: the challenge of TIM for the future of IT,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Generative AI: the challenge of TIM for the future of IT,

Reference 18

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This paper cites Empowering IoT with Generative AI: Applications, Case Studies, and Limitations,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Empowering IoT with Generative AI: Applications, Case Studies, and Limitations,

Reference 19

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This paper cites A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions.

Mapping the Landscape of Generative AI in Network Monitoring and Management A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions

Reference 20

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This paper cites Machine Learning Techniques for IoT Security: Current Research and Future Vision with Generative AI and Large Language Models,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Machine Learning Techniques for IoT Security: Current Research and Future Vision with Generative AI and Large Language Models,

Reference 21

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This paper cites Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review,

Reference 22

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This paper cites Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities.

Mapping the Landscape of Generative AI in Network Monitoring and Management Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities

Reference 23

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This paper cites At the dawn of generative AI era: A tutorial-cum-survey on new frontiers in 6G wireless intelligence,.

Mapping the Landscape of Generative AI in Network Monitoring and Management At the dawn of generative AI era: A tutorial-cum-survey on new frontiers in 6G wireless intelligence,

Reference 24

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This paper cites Generative AI in Mobile Networks: a Survey,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Generative AI in Mobile Networks: a Survey,

Reference 25

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This paper cites Large Language Models for Networking: Workflow, Advances and Challenges.

Mapping the Landscape of Generative AI in Network Monitoring and Management Large Language Models for Networking: Workflow, Advances and Challenges

Reference 26

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This paper cites Large Language Models for Networking: Applications, Enabling Techniques, and Challenges.

Mapping the Landscape of Generative AI in Network Monitoring and Management Large Language Models for Networking: Applications, Enabling Techniques, and Challenges

Reference 27

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This paper cites Tele- com’s Artificial General Intelligence (AGI) Vision: Beyond the GenAI Frontier,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Tele- com’s Artificial General Intelligence (AGI) Vision: Beyond the GenAI Frontier,

Reference 28

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This paper cites Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities.

Mapping the Landscape of Generative AI in Network Monitoring and Management Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities

Reference 29

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This paper cites Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services,

Reference 30

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This paper cites Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks.

Mapping the Landscape of Generative AI in Network Monitoring and Management Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks

Reference 31

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Mapping the Landscape of Generative AI in Network Monitoring and Management Characterization and prediction of mobile-app traffic using Markov modeling,

Reference 32

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Mapping the Landscape of Generative AI in Network Monitoring and Management Synthetic and Privacy-Preserving Traffic Trace Gen- eration using Generative AI Models for Training Network Intrusion Detection Systems,

Reference 33

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Mapping the Landscape of Generative AI in Network Monitoring and Management Knowledge enhanced GAN for IoT traffic generation,

Reference 34

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Mapping the Landscape of Generative AI in Network Monitoring and Management CFLOW-AD: real-time unsupervised anomaly detection with localization via conditional nor- malizing flows,

Reference 35

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Mapping the Landscape of Generative AI in Network Monitoring and Management Auto-encoding variational Bayes,

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Mapping the Landscape of Generative AI in Network Monitoring and Management Attention is All You Need,

Reference 37

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This paper cites NICE: Non-linear independent components estimation,.

Mapping the Landscape of Generative AI in Network Monitoring and Management NICE: Non-linear independent components estimation,

Reference 38

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This paper cites Network Traffic Generation: A Survey and Methodology,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Network Traffic Generation: A Survey and Methodology,

Reference 39

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Mapping the Landscape of Generative AI in Network Monitoring and Management Generative Adversarial Nets,

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Mapping the Landscape of Generative AI in Network Monitoring and Management Conditional Generative Adversarial Nets

Reference 41

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Mapping the Landscape of Generative AI in Network Monitoring and Management Unsupervised representation learning with deep convolutional generative adversarial networks,

Reference 42

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Mapping the Landscape of Generative AI in Network Monitoring and Management Autoencoding beyond pixels using a learned similarity metric,

Reference 43

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This paper cites Density estimation using Real NVP,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Density estimation using Real NVP,

Reference 44

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Mapping the Landscape of Generative AI in Network Monitoring and Management On the Opportunities and Risks of Foundation Models

Reference 45

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Mapping the Landscape of Generative AI in Network Monitoring and Management BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 46

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Mapping the Landscape of Generative AI in Network Monitoring and Management Improving Language Understanding by Generative Pre-Training,

Reference 47

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Mapping the Landscape of Generative AI in Network Monitoring and Management Denoising Diffusion Probabilistic Mod- els,

Reference 48

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Mapping the Landscape of Generative AI in Network Monitoring and Management Understanding diffusion objectives as the elbo with simple data augmentation,

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Mapping the Landscape of Generative AI in Network Monitoring and Management Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 50

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Mapping the Landscape of Generative AI in Network Monitoring and Management Memory-efficient fine-tuning of compressed large language models via sub-4-bit integer quantization,

Reference 51

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Mapping the Landscape of Generative AI in Network Monitoring and Management LoRa: Low-rank adaptation of large language models,

Reference 52

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This paper cites NetDiffusion: Network Data Augmenta- tion Through Protocol-Constrained Traffic Generation,.

Mapping the Landscape of Generative AI in Network Monitoring and Management NetDiffusion: Network Data Augmenta- tion Through Protocol-Constrained Traffic Generation,

Reference 53

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Mapping the Landscape of Generative AI in Network Monitoring and Management Parameter-efficient fine-tuning of large-scale pre-trained language models,

Reference 54

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This paper cites Available: https://github.com/ggerganov/ggml/blob/ master/docs/gguf.md.

Mapping the Landscape of Generative AI in Network Monitoring and Management Available: https://github.com/ggerganov/ggml/blob/ master/docs/gguf.md

Reference 55

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This paper cites Using Large Language Models to Understand Telecom Standards,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Using Large Language Models to Understand Telecom Standards,

Reference 56

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This paper cites NetGPT: Generative Pretrained Transformer for Network Traffic.

Mapping the Landscape of Generative AI in Network Monitoring and Management NetGPT: Generative Pretrained Transformer for Network Traffic

Reference 57

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This paper cites Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey,

Reference 58

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This paper cites Network Traffic Classification: Techniques, Datasets, and Challenges,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Network Traffic Classification: Techniques, Datasets, and Challenges,

Reference 59

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Observation a6d92d7d-a8bc-4f1d-b140-5a07eaab3326 · outbound

This paper cites AI- powered Internet Traffic Classification: Past, Present, and Future,.

Mapping the Landscape of Generative AI in Network Monitoring and Management AI- powered Internet Traffic Classification: Past, Present, and Future,

Reference 60

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This paper cites ET-BERT: A Con- textualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification,.

Mapping the Landscape of Generative AI in Network Monitoring and Management ET-BERT: A Con- textualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification,

Reference 61

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Observation 8227bf3f-a4b5-4af3-8da4-bafa233d5226 · outbound

This paper cites NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba.

Mapping the Landscape of Generative AI in Network Monitoring and Management NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba

Reference 62

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This paper cites A Survey on Data-driven Network Intrusion Detection,.

Mapping the Landscape of Generative AI in Network Monitoring and Management A Survey on Data-driven Network Intrusion Detection,

Reference 63

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Mapping the Landscape of Generative AI in Network Monitoring and Management FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems,

Reference 64

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This paper cites Revolutionizing Cyber Threat Detec- tion With Large Language Models: A Privacy-Preserving BERT-based Lightweight Model for IoT/IIoT Devices,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Revolutionizing Cyber Threat Detec- tion With Large Language Models: A Privacy-Preserving BERT-based Lightweight Model for IoT/IIoT Devices,

Reference 65

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Mapping the Landscape of Generative AI in Network Monitoring and Management A Survey on Automated Log Analysis for Reliability Engineering,

Reference 66

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Mapping the Landscape of Generative AI in Network Monitoring and Management LogGPT: Log Anomaly Detection via GPT,

Reference 67

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Mapping the Landscape of Generative AI in Network Monitoring and Management LogPrécis: Unleashing Language Models for Automated Malicious Log Analysis: Précis: A Concise Summary of Essential Points, Statements, or Facts,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-08T04:38:57.256994Z

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This paper cites Network Management Challenges and Trends in Multi-Layer and Multi-Vendor Settings for Carrier-Grade Networks,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Network Management Challenges and Trends in Multi-Layer and Multi-Vendor Settings for Carrier-Grade Networks,

Reference 69

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Mapping the Landscape of Generative AI in Network Monitoring and Management Network Management in the Era of Ecosystems: Systematic Review and Management Framework,

Reference 70

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Mapping the Landscape of Generative AI in Network Monitoring and Management A Review of IoT Network Management: Current Status and Perspectives,

Reference 71

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Mapping the Landscape of Generative AI in Network Monitoring and Management Network Meets ChatGPT: Intent Autonomous Management, Control and Operation,

Reference 72

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Mapping the Landscape of Generative AI in Network Monitoring and Management New Directions in Automated Traffic Analysis,

Reference 73

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This paper cites Flowpic: A generic representation for encrypted traffic classification and applications identification,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Flowpic: A generic representation for encrypted traffic classification and applications identification,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-08T04:38:57.283868Z

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Observation ced0bf59-93a3-46ce-8b37-01c94f160773 · outbound

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Mapping the Landscape of Generative AI in Network Monitoring and Management NetDiffus: Network Traffic Gen- eration by Diffusion Models through Time-Series Imaging,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-08T04:38:57.288258Z

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This paper cites Multi-Class Network Traffic Generators and Classifiers Based on Neural Networks,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Multi-Class Network Traffic Generators and Classifiers Based on Neural Networks,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T04:38:57.292782Z

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Mapping the Landscape of Generative AI in Network Monitoring and Management PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-08T04:38:57.297154Z

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Mapping the Landscape of Generative AI in Network Monitoring and Management LENS: A Foundation Model for Network Traffic,

Reference 78

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Mapping the Landscape of Generative AI in Network Monitoring and Management TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation

Reference 79

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This paper cites Feasibility of State Space Models for Network Traffic Generation.

Mapping the Landscape of Generative AI in Network Monitoring and Management Feasibility of State Space Models for Network Traffic Generation

Reference 80

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Observation acb81c6d-c514-4e53-98c7-c1af40b45c33 · outbound

This paper cites NetDiff: A Service-Guided Hierarchical Diffusion Model for Network Flow Trace Generation,.

Mapping the Landscape of Generative AI in Network Monitoring and Management NetDiff: A Service-Guided Hierarchical Diffusion Model for Network Flow Trace Generation,

Reference 81

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Mapping the Landscape of Generative AI in Network Monitoring and Management Lightweight diffusion model for synthe- sizing malicious network traffic,

Reference 82

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This paper cites Bench- marking of Synthetic Network Data: Reviewing Challenges and Ap- proaches,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Bench- marking of Synthetic Network Data: Reviewing Challenges and Ap- proaches,

Reference 83

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Mapping the Landscape of Generative AI in Network Monitoring and Management Imaging Time-series to Improve Classifica- tion and Imputation,

Reference 84

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This paper cites netFound: Principled Design for Network Foundation Models.

Mapping the Landscape of Generative AI in Network Monitoring and Management netFound: Principled Design for Network Foundation Models

Reference 85

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Mapping the Landscape of Generative AI in Network Monitoring and Management An LLM-based Framework for Finger- printing Internet-connected Devices,

Reference 86

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This paper cites LAMBERT: Leveraging Attention Mechanisms to Improve the BERT Fine-Tuning Model for Encrypted Traffic Classification,.

Mapping the Landscape of Generative AI in Network Monitoring and Management LAMBERT: Leveraging Attention Mechanisms to Improve the BERT Fine-Tuning Model for Encrypted Traffic Classification,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-08T04:38:57.340893Z

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This paper cites Encrypted Traffic Classification Framework Based on Albert,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Encrypted Traffic Classification Framework Based on Albert,

Reference 88

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Mapping the Landscape of Generative AI in Network Monitoring and Management Intrusion Detection Method using Bi-directional GPT for In-vehicle Controller Area Networks,

Reference 89

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This paper cites Securing Critical Infrastructures: Deep-Learning- Based Threat Detection in IIoT,.

Mapping the Landscape of Generative AI in Network Monitoring and Management Securing Critical Infrastructures: Deep-Learning- Based Threat Detection in IIoT,

Reference 90

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Mapping the Landscape of Generative AI in Network Monitoring and Management An Extreme Semi-supervised Framework Based on Transformer for Network Intrusion Detection,

Reference 91

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Mapping the Landscape of Generative AI in Network Monitoring and Management An Attack Detection Framework Based on BERT and Deep Learning,

Reference 92

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Mapping the Landscape of Generative AI in Network Monitoring and Management Network Intru- sion Detection via Flow-to-Image Conversion and Vision Transformer Classification,

Reference 93

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Mapping the Landscape of Generative AI in Network Monitoring and Management RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System,

Reference 94

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This paper cites A Security Model Based on LightGBM and Transformer to Protect Healthcare Systems from Cyberattacks,.

Mapping the Landscape of Generative AI in Network Monitoring and Management A Security Model Based on LightGBM and Transformer to Protect Healthcare Systems from Cyberattacks,

Reference 95

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Mapping the Landscape of Generative AI in Network Monitoring and Management Intrusion Detection Technology Based on Large Language Models,

Reference 96

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Mapping the Landscape of Generative AI in Network Monitoring and Management HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)

Reference 97

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Mapping the Landscape of Generative AI in Network Monitoring and Management TNN-IDS: Transformer Neural Network-based Intrusion Detection System for MQTT-enabled IoT Networks,

Reference 98

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Mapping the Landscape of Generative AI in Network Monitoring and Management Robust Unsupervised Network Intrusion Detection with Self-supervised Masked Context Reconstruction,

Reference 99

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Mapping the Landscape of Generative AI in Network Monitoring and Management A Lightweight IoT Intrusion Detection Model based on Improved BERT- of-Theseus,

Reference 100

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