NEST: A Node-Interactive Generative Emulation Framework for Synthetic Traffic Generation

Synthetic traffic generation is a fundamental technique for evaluating system performance and security resilience. However, existing approaches fail to capture the complex, interactive traffic patterns of modern applications. While recent deep learning models can synthesize individual traffic flows with high fidelity, they are fundamentally restricted to these isolated behaviors. They cannot reproduce the system-level interactions among multiple nodes, due to the state space of multi-node systems, which grows exponentially with the number of participants and renders direct modeling computationally intractable. To break this scalability barrier, we introduce NEST, a node-interactive generative emulation framework that enables multi-node synthetic traffic generation. Its key innovation circumvents exponential complexity by decomposing the problem: rather than modeling the global network, a generative model learns each node's local behavior, which a lightweight scheduler then orchestrates into coherent, interactive traffic. Evaluations show that NEST not only achieves high statistical fidelity but, for the first time, successfully reconstructs the multi-node interaction graphs of real-world applications. These generated graphs replicate the complex dependency structures of real traffic with an average similarity of 97.7%, a task fundamentally unattainable by prior single-flow models.