Packet Capture and Analysis on MEDINA, A Massively Distributed Network Data Caching Platform

Amedeo Sapio, M. Baldi, Fulvio Risso, Narendra Anand, A. Nucci
{"title":"Packet Capture and Analysis on MEDINA, A Massively Distributed Network Data Caching Platform","authors":"Amedeo Sapio, M. Baldi, Fulvio Risso, Narendra Anand, A. Nucci","doi":"10.1142/S0129626417500104","DOIUrl":null,"url":null,"abstract":"Traffic capture and analysis is key to many domains including network management, security and network forensics. Traditionally, it is performed by a dedicated device accessing traffic at a specific point within the network through a link tap or a port of a node mirroring packets. This approach is problematic because the dedicated device must be equipped with a large amount of computation and storage resources to store and analyze packets. Alternatively, in order to achieve scalability, analysis can be performed by a cluster of hosts. However, this is normally located at a remote location with respect to the observation point, hence requiring to move across the network a large volume of captured traffic. To address this problem, this paper presents an algorithm to distribute the task of capturing, processing and storing packets traversing a network across multiple packet forwarding nodes (e.g., IP routers). Essentially, our solution allows individual nodes on the path of a flow to operate on subsets of pa...","PeriodicalId":422436,"journal":{"name":"Parallel Process. Lett.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Parallel Process. Lett.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S0129626417500104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Traffic capture and analysis is key to many domains including network management, security and network forensics. Traditionally, it is performed by a dedicated device accessing traffic at a specific point within the network through a link tap or a port of a node mirroring packets. This approach is problematic because the dedicated device must be equipped with a large amount of computation and storage resources to store and analyze packets. Alternatively, in order to achieve scalability, analysis can be performed by a cluster of hosts. However, this is normally located at a remote location with respect to the observation point, hence requiring to move across the network a large volume of captured traffic. To address this problem, this paper presents an algorithm to distribute the task of capturing, processing and storing packets traversing a network across multiple packet forwarding nodes (e.g., IP routers). Essentially, our solution allows individual nodes on the path of a flow to operate on subsets of pa...
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
大规模分布式网络数据缓存平台MEDINA的数据包捕获与分析
流量捕获和分析是许多领域的关键,包括网络管理、安全和网络取证。传统上,它是由专用设备通过链路tap或节点镜像数据包的端口访问网络中特定点的流量来执行的。这种方法是有问题的,因为专用设备必须配备大量的计算和存储资源来存储和分析数据包。或者,为了实现可伸缩性,可以由主机集群执行分析。然而,相对于观测点而言,这通常位于一个遥远的位置,因此需要在网络中移动大量捕获的流量。为了解决这个问题,本文提出了一种算法,将捕获、处理和存储穿越网络的数据包的任务分配给多个数据包转发节点(例如IP路由器)。从本质上讲,我们的解决方案允许流路径上的单个节点对pa的子集进行操作。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Note to Non-adaptive Broadcasting Semi-Supervised Node Classification via Semi-Global Graph Transformer Based on Homogeneity Augmentation 4-Free Strong Digraphs with the Maximum Size Relation-aware Graph Contrastive Learning The Normalized Laplacian Spectrum of Folded Hypercube with Applications
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1