TC: Medium: Collaborative Research: Wide-Aperture Traffic Analysis for Internet Security

TC：媒介：协作研究：互联网安全的大孔径流量分析

• 批准号: 0905565
• 负责人: Mark Crovella
• 金额: $ 72.31万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905565&HistoricalAwards=false
• 关键词: TC; Medium; Collaborative; Research; Wide

TC: Medium: Collaborative Research: Wide-Aperture Traffic Analysis for Internet SecurityAmong emerging network threats, some of the most pernicious and elusive are stealthy attacks that take place at very low rates and in a targeted fashion. This project is developing methods for identifying malicious and unwanted activity in the Internet -- specifically, traffic that is low-volume and well "hidden'' among normal traffic. The approach being taken is to develop new methods for direct analysis of Internet traffic of unprecedented scope and scale. In particular, the project is designing and implementing a system that leverages high-performance cluster computing to allow application of sophisticated pattern analysis and machine learning algorithms to network traffic at the packet and flow level.An organizing principle of the system is its decomposition into data-parallel "lenses'' and more computationally challenging "pattern analysis'' components. The project is investigating the application of this architecture to dark address monitoring in traffic from core networks -- a capability that has not been possible to date.The end result of this project will be a set of tools and a running system that may be used by researchers to enable new investigations into traffic analysis, and may be used by network operators on an ongoing basis to help protect their networks.

TC：中：协作研究：互联网安全的大孔径流量分析在新兴的网络威胁中，一些最有害和最难以捉摸的是以非常低的速率和有针对性的方式发生的秘密攻击。 该项目正在开发用于识别互联网中恶意和不需要的活动的方法，具体来说，是低流量且很好地“隐藏”在正常流量中的流量。所采取的方法是开发直接分析互联网流量的新方法。特别是，该项目正在设计和实现一个利用高性能集群计算的系统，以允许在数据包和流级别对网络流量应用复杂的模式分析和机器学习算法。是它分解为数据并行“镜头”等等计算上具有挑战性的“模式分析”组件。该项目正在研究该架构在核心网络流量暗地址监控中的应用，这是迄今为止不可能实现的功能。该项目的最终结果将是一组研究人员可以使用这些工具和运行系统来对流量分析进行新的调查，并且网络运营商可以持续使用它们来帮助保护其网络。