mlsanityData to 14 Oct 2026

NetGPT: Generative Pretrained Transformer for Network Traffic

arXiv 2304.09513 ↗First posted 19 Apr 2023cs.NI cs.AI cs.LGcited by 31

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Abstract (arXiv)

All data on the Internet are transferred by network traffic, thus accurately modeling network traffic can help improve network services quality and protect data privacy. Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks. Effective pretrained models can significantly optimize the training efficiency and effectiveness of downstream tasks, such as application classification, attack detection and traffic generation. Despite the great success of pretraining in natural language processing, there is no work in the network field. Considering the diverse demands and characteristics of network traffic and network tasks, it is non-trivial to build a pretrained model for network traffic and we face various challenges, especially the heterogeneous headers and payloads in the multi-pattern network traffic and the different dependencies for contexts of diverse downstream network tasks. To tackle these challenges, in this paper, we make the first attempt to provide a generative pretrained model NetGPT for both traffic understanding and generation tasks. We propose the multi-pattern network traffic modeling to construct unified text inputs and support both traffic understanding and generation tasks. We further optimize the adaptation effect of the pretrained model to diversified tasks by shuffling header fields, segmenting packets in flows, and incorporating diverse task labels with prompts. With diverse traffic datasets from encrypted software, DNS, private industrial protocols and cryptocurrency mining, expensive experiments demonstrate the effectiveness of our NetGPT in a range of traffic understanding and generation tasks on traffic datasets, and outperform state-of-the-art baselines by a wide margin.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 19 Apr 2023.

Builds on

10 of 25 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2022ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classificationcited by 66
  5. 2018Flow-based Network Traffic Generation using Generative Adversarial Networkscited by 26
  6. 2021Realtime Robust Malicious Traffic Detection via Frequency Domain Analysiscited by 20
  7. 2020Autonomous Unknown-Application Filtering and Labeling for DL-based Traffic Classifier Updatecited by 16
  8. 2020STAN: Synthetic Network Traffic Generation with Generative Neural Modelscited by 11
  9. 2019HEDGE: Efficient Traffic Classification of Encrypted and Compressed Packetscited by 5
  10. 2021Synthetic flow-based cryptomining attack generation through Generative Adversarial Networkscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 11; 2026: 11.

31 papers.

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  1. 2024NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mambacited by 24
  2. 2023FLM-101B: An Open LLM and How to Train It with 100K Budgetcited by 22
  3. 2024TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generationcited by 15
  4. 2024Lens: A Knowledge-Guided Foundation Model for Network Trafficcited by 7
  5. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
  6. 2025Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directionscited by 4
  7. 2025Quantifying the Privacy Implications of High-Fidelity Synthetic Network Trafficcited by 3
  8. 2024Network Anomaly Traffic Detection via Multi-view Feature Fusioncited by 2
  9. 2024TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Networkcited by 2
  10. 2024NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamicscited by 2
  11. 2025Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systemscited by 2
  12. 2025A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learningcited by 2
19 more
  1. 2025Demystifying Network Foundation Modelscited by 2
  2. 2026PLUME: Building a Network-Native Foundation Model for Wireless Traces via Protocol-Aware Tokenizationcited by 2
  3. 2026CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drivescited by 2
  4. 2024MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classificationcited by 1
  5. 2025Application of Tabular Transformer Architectures for Operating System Fingerprintingcited by 1
  6. 2025UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Securitycited by 1
  7. 2026NetMamba+: A Framework of Pre-trained Models for Efficient and Accurate Network Traffic Classificationcited by 1
  8. 2026TrafficMoE: Heterogeneity-aware Mixture of Experts for Encrypted Traffic Classificationcited by 1
  9. 2026UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shiftscited by 1
  10. 2024SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuingcited by 0
  11. 2025Harnessing Generative Pre-Trained Transformer for Datacenter Packet Trace Generationcited by 0
  12. 2025Versatile yet Efficient Network Traffic Analysis: Offloading Network Foundation Model to SmartNICcited by 0
  13. 2025MalRAG: A Retrieval-Augmented LLM Framework for Open-set Malicious Traffic Identificationcited by 0
  14. 2026Improving DNS Exfiltration Detection via Transformer Pretrainingcited by 0
  15. 2026MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretrainingcited by 0
  16. 2026Contrastive Learning and Correlation Clustering for Sequences of Network Telescope Datacited by 0
  17. 2026Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Modelscited by 0
  18. 2026PCAP-LM: An LLM-Native Text Representation for TLS Bulk Traffic Analysiscited by 0
  19. 2026Robust Conformal Intrusion Detection via Traffic-Aware Calibration and Attack-Orbit Invariancecited by 0