CascadeNet: Generating Network Traffic with High-Fidelity Temporal Patterns

Facing the challenge of limited network trace access, the exploration of synthetic trace generation has become crucial for research. Although current methods manage to replicate the statistical characteristics of network traffic accurately, they fail to capture the temporal dynamics of network activities. This gap stems primarily from their approach to data representation. To address this issue, we propose a novel representation of network traces by aggregating network flows into time series. Built upon this data representation, we propose CascadeNet, an end-to-end framework embedded with CascadeGAN—a hierarchical generative model—to generate network traffic with high-fidelity temporal patterns while learning complex flow structures and dependencies. We also develop several techniques to facilitate the transformation from aggregated time series to timestamps. Our evaluations across four diverse IPv4 header traces show (1) CascadeNet surpasses baselines by 41%~76% on temporal distance metrics; (2) CascadeNet outperforms baselines in downstream tasks; (3) it offers remarkable scalability, reducing training time by 7.3×~25× compared to state-of-the-art method.