research-article

NetDiff: A Service-Guided Hierarchical Diffusion Model for Network Flow Trace Generation

Authors:

Yong LiAuthors Info & Claims

Proceedings of the ACM on Networking, Volume 2, Issue CoNEXT3

Article No.: 16, Pages 1 - 21

https://doi.org/10.1145/3676870

Published: 21 August 2024 Publication History

Abstract

Network flow traces are fundamental to many network management workflows. In this paper, we aim to generate high-fidelity network flow traces by explicitly modeling users' dynamic network usage intents. We propose NetDiff, a service-guided hierarchical diffusion model for network flow trace generation. NetDiff employs a hierarchical generation structure that includes a layer to model mobile users' interactions with network services, such as app usage traces, and leverages these generated app usage traces to guide network flow generation. NetDiff avoids pattern collapse and generates controlled samples by gradually eliminating noise and using service conditions to guide each step more precisely. It captures the co-usage and sequential relationships across network service usage through a pre-trained embedding model and an encoder-decoder structure. Additionally, NetDiff captures the temporal and feature correlations present in multidimensional network flow data through a two-layer transformer network. Extensive experiments on real-world network flow datasets demonstrate that NetDiff significantly outperforms state-of-the-art baselines regarding Jensen-Shannon divergence, total variation distance, and cumulative residual probability sum squares. Furthermore, NetDiff is robust across various datasets from different cities, meeting users' requirements for downstream tasks by maintaining algorithm accuracy and order.

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Cited By

Mehmood SAmin RMustafa JHussain MAlsubaei FZakaria M(2025)Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLPPLOS ONE10.1371/journal.pone.031242520:1(e0312425)Online publication date: 27-Jan-2025
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Index Terms

NetDiff: A Service-Guided Hierarchical Diffusion Model for Network Flow Trace Generation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Networks
  1. Network services
    1. Network management
  2. Network types
    1. Mobile networks

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cover image Proceedings of the ACM on Networking

Proceedings of the ACM on Networking Volume 2, Issue CoNEXT3

PACMNET

September 2024

108 pages

EISSN:2834-5509

DOI:10.1145/3689614

Editors:
Marco Mellia
Politecnico di Torino, Italy
,
Peter Steenkiste
Carnegie Mellon University, United States

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Published: 21 August 2024

Published in PACMNET Volume 2, Issue CoNEXT3

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https://doi.org/10.1371/journal.pone.0312425
Li TLong QChai HZhang SJiang FLiu HHuang WJin DLi Y(2025)Generative AI Empowered Network Digital Twins: Architecture, Technologies, and ApplicationsACM Computing Surveys10.1145/371168257:6(1-43)Online publication date: 10-Jan-2025
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Isaac ESingh B(2025)QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)10.1109/COMSNETS63942.2025.10885747(847-851)Online publication date: 6-Jan-2025
https://doi.org/10.1109/COMSNETS63942.2025.10885747
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