Collaborative ITR/CSE: Modular Strategies for Internetwork Monitoring

协作 ITR/CSE：互联网监控的模块化策略

• 批准号: 0325571
• 负责人: Alfred Hero
• 金额: $ 147.28万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325571&HistoricalAwards=false
• 关键词: Collaborative; ITR; CSE; Modular; Strategies

ABSTRACT0325571Alfred HeroU of MIchigan Ann ArborThis project addresses the longstanding and difficult problem of detecting and classifying spatially distributed network anomalies from multiple monitoring sites. To characterize baseline vs. anomalous behavior of the Internet requires deployment of collaborative data collection, anomaly detection and pattern recognition for complex large scale systems. The project combines the forces of leading researchers in three complementary disciplines: (i) networking and data collection; (ii) statistical data analysis and signal processing; (iii) decentralized decision-making. The research goes well beyond the state-of-the art anomaly detection for centrally administered networks. In particular tools and practical data sharing algorithms are being developed for detecting coordinated intrusions, distributed denial of service attacks, and quality-of-service degradations in decentralized networks such as the Internet. The project also includes activities with broader impact including: creation of a public network anomaly database, K-12 educational outreach, and university-industry collaborations.The research approach is based on a modular and distributed monitoring paradigm that is organized into a three level hierarchy: local level measurement of data from servers, routers and switches; intermediate level data analysis and processing of end-to-end traffic measurements, summary statistics and alarms transmitted from the local level; and upper level decision-making and processing of information transmitted from the intermediate level. This modular structure is scalable to large networks of monitoring sites. However, this structure also imposes constraints on data analysis which requires development of new approaches. Three approaches are being pursued: distributed spatio-temporal data analysis using wavelets over graphs; event detection and classification using distributed pattern analysis and learning; and multi-site event correlation using discrete event dynamical systems and decentralized stochastic systems.

这个项目解决了从多个监测站点检测和分类空间分布的网络异常的长期而困难的问题。 为了表征互联网的基线与异常行为，需要部署复杂的大规模系统的协作数据收集，异常检测和模式识别。该项目结合了三个互补学科的主要研究人员的力量：㈠联网和数据收集; ㈡统计数据分析和信号处理; ㈢分散决策。该研究远远超出了中央管理网络的最先进的异常检测。特别是正在开发的工具和实用的数据共享算法，用于检测协同入侵，分布式拒绝服务攻击，并在分散的网络，如互联网的服务质量下降。 该项目还包括具有更广泛影响的活动，包括：创建一个公共网络异常数据库，K-12教育推广，以及大学和行业的合作。研究方法是基于一个模块化和分布式的监测范式，分为三个层次：本地级测量来自服务器，路由器和交换机的数据;中间层数据分析和处理从本地层传输的端到端流量测量、汇总统计和警报;以及上层决策和处理从中间层传输的信息。 这种模块化结构可扩展到大型监测站点网络。然而，这种结构也给数据分析带来了限制，这就需要开发新的方法。目前正在采用三种方法：使用小波对图形进行分布式时空数据分析;使用分布式模式分析和学习进行事件检测和分类;使用离散事件动态系统和分散随机系统进行多站点事件关联。