Towards Large Scale Packet Capture and Network Flow Analysis on Hadoop

M. Z. N. L. Saavedra, W. E. Yu
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引用次数: 4

Abstract

Network traffic continues to grow yearly at a compounded rate. However, network traffic is still being analyzed on vertically scaled machines that do not scale as well as distributed computing platforms. Hadoop's horizontally scalable ecosystem provides a better environment for processing these network captures stored in packet capture (PCAP) files. This paper proposes a framework called hcap for analyzing PCAPs on Hadoop inspired by the Rseaux IP Europens' (RIPE's) existing hadoop-pcap library but built completely from the ground up. The hcap framework improves several aspects of the hadoop-pcap library, namely protocol, error, and log handling. Results show that, while other methods still outperform hcap, it not only performs better than hadoop-pcap by 15% in scan queries and 18% in join queries, but it's more tolerant to broken PCAP entries which reduces preprocessing time and data loss, while also speeding up the conversion process used in other methods by 85%.
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面向Hadoop的大规模数据包捕获和网络流分析
网络流量每年继续以复合速度增长。然而,网络流量仍然是在垂直扩展的机器上进行分析的,这种机器的扩展能力不如分布式计算平台。Hadoop的水平可扩展生态系统为处理存储在包捕获(PCAP)文件中的网络捕获提供了更好的环境。本文提出了一个名为hcap的框架,用于分析Hadoop上的pcap,该框架的灵感来自Rseaux IP Europens (RIPE)现有的Hadoop -pcap库,但完全是从头开始构建的。hcap框架改进了hadoop-pcap库的几个方面,即协议、错误和日志处理。结果表明,虽然其他方法的性能仍然优于hcap,但hcap不仅在扫描查询方面比hadoop-pcap性能好15%,在连接查询方面比hadoop-pcap性能好18%,而且它对PCAP条目的损坏更宽容,从而减少了预处理时间和数据丢失,同时还将其他方法中使用的转换过程加快了85%。
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