NetSSM: Multi-Flow and State-Aware Network Trace Generation using State-Space Models

Access to raw network traffic data is essential for many computer networking tasks, from traffic modeling to performance evaluation. Unfortunately, this data is scarce due to high collection costs and governance rules. and lack robust evaluations tied to real-world utility. We propose a new method based on state space models called NetSSM that generates raw network traffic at the packet-level granularity. Our approach captures interactions between multiple, interleaved flows -- an objective unexplored in prior work -- and effectively reasons about flow-state in sessions to capture traffic characteristics. NetSSM accomplishes this by training with a context window more than 8× longer, and produces traces up to 78× longer than existing transformer-based raw packet generators. compliance with standard protocol requirements and flow and session-level traffic characteristics.