用自动学习规则压缩网络数据，省去人工设计且效果更好
Learning Compression Rules for Network Traffic
- 通过熵比递归分割流量，发现冗余模式
- 动态规划选出最少规则下压缩率最高的组合
- 在物联网和5G核心网数据上表现超越人工规则集
本文研究结构化网络流量的紧凑规则压缩方法。每个数据包包含高度冗余的头字段，压缩器由一组匹配这些记录的规则组成，将可预测字段替换为短码。我们将规则学习建模为两阶段问题：(i) 无监督结构发现阶段，使用对小样本鲁棒的归一化熵比准则递归划分训练数据包；(ii) 受限选择阶段，利用动态规划在规则数量受限的前提下选取最大化预期压缩收益的规则子集。该框架应用于IETF标准静态上下文头压缩（SCHC），在四个真实世界物联网与5G核心网数据集上评估。所提方法RECAP（Robust Entropy Clustering for Adaptive comPression）仅用少量学习规则即超越人工设计规则集，无需手动规则设计。
原文摘要 · Abstract (English)
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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