Learning Compression Rules for Network Traffic

Abstract:We study the problem of learning compact rule-based compressors for structured network traffic. Each packet is a record of header fields that are highly redundant within a flow, and a compressor is a small set of rules matching such records and replacing predictable fields with short codes. We cast rule learning as a two-stage problem: (i) an unsupervised structure-discovery stage that recursively partitions training packets using a normalized entropy-ratio criterion robust to small samples, and (ii) a constrained selection stage that uses dynamic programming to pick the rule subset maximizing expected compression gain under a hard budget on the number of installable rules. We instantiate the framework on Static Context Header Compression (SCHC), the IETF standard for rule-based header compression in constrained networks, and evaluate it on four real-world Internet-of-Things and 5G core-network datasets. Our method, Robust Entropy Clustering for Adaptive comPression (RECAP), surpasses expert-engineered rule sets with a small number of learned rules and removes the need for manual rule design.

Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)

arXiv:2608.04545 [cs.LG]

(or arXiv:2608.04545v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.04545 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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