NetSSM: Multi-Flow and State-Aware Network Trace Generation using State-Space Models
Abstract
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.
Fields of Study & Subjects
Software-Defined Networks and 5G
Network Traffic and Congestion Control
Internet Traffic Analysis and Secure E-voting
Publication details
- Journal / source
- Proceedings of the ACM on Networking
- Publisher
- Association for Computing Machinery (ACM)
- Volume
- 4
- Issue
- CoNEXT1
- Pages
- 1–24
- Publication date
- 25 March 2026
- Language
- EN