用自编码器压缩网络流量数据，不降精度还能省存储。
AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification
- 用自编码器直接压缩流量记录，无需解压即可分析。
- 压缩后数据量减小1.313倍，分类准确率仍达99.27%。
- 适合需要高效监控与实时分析的网络系统部署。
网络监控产生海量IP流记录，给存储与分析带来巨大挑战。本文提出一种基于自编码器的深度学习压缩方法，可直接对压缩数据进行分析，无需解压。与传统压缩方式不同，该方法在降低数据量的同时，保持了压缩数据对下游任务（如区分现代应用协议和主流服务加密流量）的可用性。在真实网络流量数据集上的大量实验表明，该自编码器压缩方法实现1.313倍的数据量缩减，同时在多类流量分类任务中保持99.27%的准确率，相比未压缩数据的99.77%仅略有下降。这一微小性能损失被显著提升的存储与处理效率所抵消。本工作为更高效的网络监控及可扩展的实时网络管理提供了支持。
原文摘要 · Abstract (English)
Network monitoring generates massive volumes of IP flow records, posing significant challenges for storage and analysis. This paper presents a novel deep learning-based approach to compressing these records using autoencoders, enabling direct analysis of compressed data without requiring decompression. Unlike traditional compression methods, our approach reduces data volume while retaining the utility of compressed data for downstream analysis tasks, including distinguishing modern application protocols and encrypted traffic from popular services. Through extensive experiments on a real-world network traffic dataset, we demonstrate that our autoencoder-based compression achieves a 1.313x reduction in data size while maintaining 99.27% accuracy in a multi-class traffic classification task, compared to 99.77% accuracy with uncompressed data. This marginal decrease in performance is offset by substantial gains in storage and processing efficiency. The implications of this work extend to more efficient network monitoring and scalable, real-time network management solutions.
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