# Flow-based Network Traffic Generation using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1810.07795 · HTML: https://mlsanity.com/paper/1810.07795/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 26 of 26)

- [TabMT: Generating tabular data with masked transformers](https://mlsanity.com/paper/2312.06089/) (2023)
- [NetGPT: Generative Pretrained Transformer for Network Traffic](https://mlsanity.com/paper/2304.09513/) (2023)
- [TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation](https://mlsanity.com/paper/2403.05822/) (2024)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Lens: A Knowledge-Guided Foundation Model for Network Traffic](https://mlsanity.com/paper/2402.03646/) (2024)
- [Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks](https://mlsanity.com/paper/2107.14776/) (2021)
- [UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization](https://mlsanity.com/paper/2410.15322/) (2024)
- [Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet Detection](https://mlsanity.com/paper/2210.02840/) (2022)
- [Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing Flows](https://mlsanity.com/paper/2403.10550/) (2024)
- [NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics](https://mlsanity.com/paper/2412.20635/) (2024)
- [Making Logic a First-Class Citizen in Generative ML for Networking](https://mlsanity.com/paper/2506.23964/) (2025)
- [A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning](https://mlsanity.com/paper/2507.01976/) (2025)
- [Log2NS: Enhancing Deep Learning Based Analysis of Logs With Formal to Prevent Survivorship Bias](https://mlsanity.com/paper/2105.14149/) (2021)
- [Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue](https://mlsanity.com/paper/2203.11410/) (2022)
- [DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection](https://mlsanity.com/paper/2301.03368/) (2023)
- [Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic](https://mlsanity.com/paper/2408.16034/) (2024)
- [Multi-Scale Diffusion Transformer for Jointly Simulating User Mobility and Mobile Traffic Pattern](https://mlsanity.com/paper/2510.10158/) (2025)
- [CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks](https://mlsanity.com/paper/2402.17363/) (2024)
- [Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis](https://mlsanity.com/paper/2411.01929/) (2024)
- [Harnessing Generative Pre-Trained Transformer for Datacenter Packet Trace Generation](https://mlsanity.com/paper/2501.12033/) (2025)
- [Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generation](https://mlsanity.com/paper/2505.07777/) (2025)
- [Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion](https://mlsanity.com/paper/2507.20115/) (2025)
- [Semantic-aware Graph-guided Behavior Sequences Generation with Large Language Models for Smart Homes](https://mlsanity.com/paper/2508.03484/) (2025)
- [Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks](https://mlsanity.com/paper/2508.19495/) (2025)
- [TAO-Net: Two-stage Adaptive OOD Classification Network for Fine-grained Encrypted Traffic Classification](https://mlsanity.com/paper/2512.15753/) (2025)
- [Evaluating Tabular Representation Learning for Network Intrusion Detection](https://mlsanity.com/paper/2605.02519/) (2026)

## 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.