NetSE Small: Unsupervised flow-based clustering

NetSE Small：无监督的基于流的集群

• 批准号: 0915552
• 负责人: George Kesidis
• 金额: $ 29.93万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915552&HistoricalAwards=false
• 关键词: NetSE; Small; Unsupervised; flow; based

Operators of large-scale enterprises and ISPs need to understand the type of traffic that their networks handle, and emergent applications and traffic behavior, in order to better service them and detect anomalies. Also, better assessment of the carried traffic will inform network planning and security. Particularly for private enterprise networks, monitoring methods can be used to detect inappropriate traffic classes indicating unprofessional (or at least unauthorized) activity. This work will adapt and innovate methods of unsupervised machine learning to classify traffic flows to ascertain the types of end-user applications which are active in an enterprise network. The broader impact of the project will include explaining networking concepts to a wider audience of machine learning researchers, and vice versa so that the newly developed techniques will have wide dissemination to the networking community, as well as to other domains in science and engineering. Also, cross-disciplinary graduate-level courseware on applications of machine learning to network flow data and related concepts will be developed and disseminated. More practical developments will be achieved through collaboration with industrial partners. Finally, the project will aim to support graduate students from under-represented groups in computer science and engineering, particularly women.The primary technical merit of the research to be conducted will pertain to the high-volume and considerably complex network data under consideration, including prevalent short flows, given limited computing and communication resources to do so.

大型企业和ISP的运营商需要了解其网络处理的流量类型，以及新兴的应用程序和流量行为，以便更好地为其服务并检测异常。此外，对携带流量的更好评估将为网络规划和安全性提供信息。特别是对于私人企业网络，监视方法可用于检测不适当的交通类别，表明非专业（或至少未经授权）活动。这项工作将适应无监督的机器学习的方法，以对流量流进行分类，以确定企业网络中活动的最终用户应用程序的类型。该项目的更广泛影响将包括向更广泛的机器学习研究人员解释网络概念，反之亦然，以使新开发的技术将对网络社区以及科学和工程领域的其他领域进行广泛传播。此外，将开发和传播跨学科的研究生级课程在机器学习到网络流数据和相关概念的应用。通过与工业合作伙伴的合作，将实现更多实际的发展。最后，该项目旨在为来自计算机科学和工程中代表性不足的群体（尤其是女性）的研究生提供支持。进行的研究的主要技术优点将与正在考虑的高容量和相当复杂的网络数据有关，包括普遍的短流，鉴于有限的计算和通信资源来实现这一问题。