关联分析:针对互联网骨干网的弹性流量分析解决方案

Marios Iliofotou, Brian Gallagher, Tina Eliassi-Rad, Guowu Xie, M. Faloutsos
{"title":"关联分析:针对互联网骨干网的弹性流量分析解决方案","authors":"Marios Iliofotou, Brian Gallagher, Tina Eliassi-Rad, Guowu Xie, M. Faloutsos","doi":"10.1145/1921168.1921171","DOIUrl":null,"url":null,"abstract":"Profiling Internet backbone traffic is becoming an increasingly hard problem since users and applications are avoiding detection using traffic obfuscation and encryption. The key question addressed here is: Is it possible to profile traffic at the backbone without relying on its packet and flow level information, which can be obfuscated? We propose a novel approach, called Profiling-By-Association (PBA), that uses only the IP-to-IP communication graph and information about some applications used by few IP-hosts (a.k.a. seeds). The key insight is that IP-hosts tend to communicate more frequently with hosts involved in the same application forming communities (or clusters). Profiling few members within a cluster can \"give away\" the whole community. Following our approach, we develop different algorithms to profile Internet traffic and evaluate them on real-traces from four large backbone networks. We show that PBA's accuracy is on average around 90% with knowledge of only 1% of all the hosts in a given data set and its runtime is on the order of minutes (≈ 5).","PeriodicalId":20688,"journal":{"name":"Proceedings of The 6th International Conference on Innovation in Science and Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":"{\"title\":\"Profiling-By-Association: a resilient traffic profiling solution for the internet backbone\",\"authors\":\"Marios Iliofotou, Brian Gallagher, Tina Eliassi-Rad, Guowu Xie, M. Faloutsos\",\"doi\":\"10.1145/1921168.1921171\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Profiling Internet backbone traffic is becoming an increasingly hard problem since users and applications are avoiding detection using traffic obfuscation and encryption. The key question addressed here is: Is it possible to profile traffic at the backbone without relying on its packet and flow level information, which can be obfuscated? We propose a novel approach, called Profiling-By-Association (PBA), that uses only the IP-to-IP communication graph and information about some applications used by few IP-hosts (a.k.a. seeds). The key insight is that IP-hosts tend to communicate more frequently with hosts involved in the same application forming communities (or clusters). Profiling few members within a cluster can \\\"give away\\\" the whole community. Following our approach, we develop different algorithms to profile Internet traffic and evaluate them on real-traces from four large backbone networks. We show that PBA's accuracy is on average around 90% with knowledge of only 1% of all the hosts in a given data set and its runtime is on the order of minutes (≈ 5).\",\"PeriodicalId\":20688,\"journal\":{\"name\":\"Proceedings of The 6th International Conference on Innovation in Science and Technology\",\"volume\":\"49 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"42\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of The 6th International Conference on Innovation in Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1921168.1921171\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The 6th International Conference on Innovation in Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1921168.1921171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 42

摘要

由于用户和应用程序使用流量混淆和加密来避免检测,对互联网骨干流量进行分析已成为越来越困难的问题。这里要解决的关键问题是:是否有可能在不依赖于可能被混淆的分组和流级信息的情况下对骨干网络的流量进行分析?我们提出了一种新的方法,称为关联分析(PBA),它只使用ip到ip通信图和关于少数ip主机(又名种子)使用的一些应用程序的信息。关键是ip主机倾向于更频繁地与组成社区(或集群)的相同应用程序中的主机进行通信。分析集群中的少数成员可能会“泄露”整个社区。根据我们的方法,我们开发了不同的算法来分析互联网流量,并在四个大型骨干网的真实轨迹上对其进行评估。我们表明,PBA的准确率平均约为90%,仅了解给定数据集中所有主机的1%,其运行时间约为分钟(≈5)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Profiling-By-Association: a resilient traffic profiling solution for the internet backbone
Profiling Internet backbone traffic is becoming an increasingly hard problem since users and applications are avoiding detection using traffic obfuscation and encryption. The key question addressed here is: Is it possible to profile traffic at the backbone without relying on its packet and flow level information, which can be obfuscated? We propose a novel approach, called Profiling-By-Association (PBA), that uses only the IP-to-IP communication graph and information about some applications used by few IP-hosts (a.k.a. seeds). The key insight is that IP-hosts tend to communicate more frequently with hosts involved in the same application forming communities (or clusters). Profiling few members within a cluster can "give away" the whole community. Following our approach, we develop different algorithms to profile Internet traffic and evaluate them on real-traces from four large backbone networks. We show that PBA's accuracy is on average around 90% with knowledge of only 1% of all the hosts in a given data set and its runtime is on the order of minutes (≈ 5).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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