中圖分類號：TP309文獻標識碼：ADOI:10.19358/j.issn.2097-1788.2024.06.005
引用格式：康未，李維皓，劉桐菊.基于生成對抗神經網絡的流量生成方法研究［J］.網絡安全與數據治理，2024，43（6）：33-41.

Traffic generation methods based on generative adversarial neural networks

Abstract： Traffic generation in network simulation is crucial for ensuring simulation effectiveness. Currently, common network traffic generators are typically based on a certain random model, where the generated traffic adheres to a specified random distribution. However, determining a realistic random model for actual network traffic is often challenging, leading to biases in current models when simulating real network traffic. To address these issues, this paper proposes a spatiotemporal-correlated traffic generation model based on Generative Adversarial Neural Networks (GANs). The encoding method for network traffic data is improved, and Z-score is applied to process traffic data, making the data tend toward a standard normal distribution. Additionally, a measurement method for evaluating the spatiotemporal correlation of network traffic is introduced. Experimental results indicate that, compared to existing baseline generation methods, the proposed approach averages 9% improvement in measures of authenticity and correlation.

Key words : network simulation; network traffic generation; generative adversarial neural networks; spatiotemporal correlation