Network traffic analysis increasingly relies on feature-based representations to support monitoring and security in the presence of pervasive encryption. Although features are more compact than raw packet traces, their storage has become a scalability bottleneck from large-scale core networks to resource-constrained Internet of Things (IoT) environments. This article investigates task-aware lossy compression strategies that reduce the storage footprint of traffic features while preserving analytics accuracy. Using website classification in core networks and device identification in IoT environments as representative use cases, we show that simple, semantics-preserving compression techniques expose stable operating regions that balance storage efficiency and task performance. These results highlight compression as a first-class design dimension in scalable network monitoring systems.

翻译：随着加密技术的普及，网络流量分析日益依赖基于特征的表示来支持监控与安全。尽管特征数据比原始数据包追踪更为紧凑，但其存储已成为从大规模核心网络到资源受限的物联网（IoT）环境中的可扩展性瓶颈。本文研究了任务感知的有损压缩策略，以减少流量特征的存储占用，同时保持分析准确性。以核心网络中的网站分类和物联网环境中的设备识别作为代表性用例，我们证明简单的语义保持压缩技术能够揭示稳定的操作区间，从而平衡存储效率与任务性能。这些结果凸显了压缩作为可扩展网络监控系统中首要设计维度的重要性。