Learning Compression Rules for Network Traffic

arXiv:2608.04545 · cs.LG, cs.NI · Submitted 2026-08-05 · Read on arXiv

Quentin Lampin, Éloi Sainte-Beuve, Louis-Adrien Dufrène, Guillaume Larue, Massih-Reza Amini

Orange Research · Université Grenoble Alpes · Orange Research · Orange Research · Université Grenoble Alpes

cs.LG, cs.NI

Submitted: 2026-08-05

License: http://creativecommons.org/licenses/by-nc-sa/4.0/

The gist: We study the problem of learning compact rule-based compressors for structured network traffic.

Terminology

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.

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