学习网络流量的压缩规则

中文摘要 AI 辅助

我们研究针对结构化网络流量学习紧凑的基于规则的压缩器的问题。每个数据包是一个包含头部字段的记录，这些字段在流内具有高度冗余性，而压缩器是一小套规则，用于匹配此类记录并将可预测字段替换为短编码。我们将规则学习转化为两阶段问题：（i）无监督结构发现阶段，使用对小样本具有鲁棒性的归一化熵比准则递归划分训练数据包；（ii）约束选择阶段，使用动态规划选择规则子集，在可安装规则数量的硬预算约束下最大化预期压缩增益。我们将该框架实例化为静态上下文头部压缩（SCHC，互联网工程任务组针对受限网络中基于规则的头部压缩的标准），并在四个真实世界的物联网和5G核心网络数据集上进行评估。我们的方法——用于自适应压缩的鲁棒熵聚类（RECAP），仅用少量学习得到的规则就超过了专家设计的规则集，且消除了手动规则设计的需求。

英文摘要

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.

发表机构

• Orange Research（奥兰治研究中心）
• Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）

机构由 AI 辅助整理，请以论文原文为准。