mlsanityData to 7 Oct 2026

Flow-based Network Traffic Generation using Generative Adversarial Networks

arXiv 1810.07795 ↗First posted 27 Sep 2018cs.NI stat.MLcited by 26

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Abstract (arXiv)

Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major challenge lies in the fact that GANs can only process continuous attributes. However, flow-based data inevitably contain categorical attributes such as IP addresses or port numbers. Therefore, we propose three different preprocessing approaches for flow-based data in order to transform them into continuous values. Further, we present a new method for evaluating the generated flow-based network traffic which uses domain knowledge to define quality tests. We use the three approaches for generating flow-based network traffic based on the CIDDS-001 data set. Experiments indicate that two of the three approaches are able to generate high quality data.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2018.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 8; 2025: 9; 2026: 1.

26 papers.

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  1. 2023TabMT: Generating tabular data with masked transformerscited by 36
  2. 2023NetGPT: Generative Pretrained Transformer for Network Trafficcited by 31
  3. 2024TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generationcited by 15
  4. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  5. 2024Lens: A Knowledge-Guided Foundation Model for Network Trafficcited by 7
  6. 2021Synthetic flow-based cryptomining attack generation through Generative Adversarial Networkscited by 4
  7. 2024UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimizationcited by 4
  8. 2022Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detectioncited by 3
  9. 2024Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flowscited by 3
  10. 2024NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamicscited by 2
  11. 2025A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learningcited by 2
  12. 2021Log2NS: Enhancing Deep Learning Based Analysis of Logs With Formal to Prevent Survivorship Biascited by 1
14 more
  1. 2022Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issuecited by 1
  2. 2023DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detectioncited by 1
  3. 2024Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Trafficcited by 1
  4. 2025Making Logic a First-Class Citizen in Generative ML for Networkingcited by 1
  5. 2025Multi-Scale Diffusion Transformer for Jointly Simulating User Mobility and Mobile Traffic Patterncited by 1
  6. 2024CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networkscited by 0
  7. 2024Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesiscited by 0
  8. 2025Harnessing Generative Pre-Trained Transformer for Datacenter Packet Trace Generationcited by 0
  9. 2025Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generationcited by 0
  10. 2025Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusioncited by 0
  11. 2025Semantic-aware Graph-guided Behavior Sequences Generation with Large Language Models for Smart Homescited by 0
  12. 2025Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networkscited by 0
  13. 2025TAO-Net: Two-stage Adaptive OOD Classification Network for Fine-grained Encrypted Traffic Classificationcited by 0
  14. 2026Evaluating Tabular Representation Learning for Network Intrusion Detectioncited by 0