Real-time analysis of NetFlow data for generating network traffic statistics using Apache Spark

Milan Cermák, Tomás Jirsík, Martin Laštovička
{"title":"Real-time analysis of NetFlow data for generating network traffic statistics using Apache Spark","authors":"Milan Cermák, Tomás Jirsík, Martin Laštovička","doi":"10.1109/NOMS.2016.7502952","DOIUrl":null,"url":null,"abstract":"In this paper, we present a framework for the real-time generation of network traffic statistics on Apache Spark Streaming, a modern distributed stream processing system. Our previous results showed that stream processing systems provide enough throughput to process a large volume of NetFlow data and hence they are suitable for network traffic monitoring. This paper describes the integration of Apache Spark Streaming into a current network monitoring architecture. We prove that it is possible to implement the same basic methods for NetFlow data analysis in the stream processing framework as in the traditional ones. Moreover, our stream processing implementation discovers new information which is not available when using traditional network monitoring approaches.","PeriodicalId":344879,"journal":{"name":"NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NOMS.2016.7502952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

In this paper, we present a framework for the real-time generation of network traffic statistics on Apache Spark Streaming, a modern distributed stream processing system. Our previous results showed that stream processing systems provide enough throughput to process a large volume of NetFlow data and hence they are suitable for network traffic monitoring. This paper describes the integration of Apache Spark Streaming into a current network monitoring architecture. We prove that it is possible to implement the same basic methods for NetFlow data analysis in the stream processing framework as in the traditional ones. Moreover, our stream processing implementation discovers new information which is not available when using traditional network monitoring approaches.
使用Apache Spark实时分析NetFlow数据,生成网络流量统计数据
在本文中,我们提出了一个在Apache Spark Streaming(一个现代分布式流处理系统)上实时生成网络流量统计数据的框架。我们之前的研究结果表明,流处理系统提供了足够的吞吐量来处理大量的NetFlow数据,因此它们适合于网络流量监控。本文描述了Apache Spark Streaming与当前网络监控体系结构的集成。我们证明了在流处理框架中实现与传统流处理框架相同的NetFlow数据分析基本方法是可能的。此外,我们的流处理实现发现了传统网络监控方法无法获得的新信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信