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This paper addresses the challenge of learning compact rule-based compressors for structured network traffic by introducing a two-stage approach that combines unsupervised structure discovery with constrained rule selection. The authors present Robust Entropy Clustering for Adaptive comPression (RECAP), which effectively partitions training packets and selects a minimal set of rules to maximize compression gain, outperforming traditional expert-engineered methods. Evaluated on real-world IoT and 5G datasets, RECAP demonstrates significant improvements in compression efficiency while eliminating the need for manual rule design.
RECAP achieves superior network traffic compression by learning rules that outperform expert designs, all while minimizing manual intervention.
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.
