Collaborative ITR/CSE: Modular Strategies for Internetwork Monitoring

来自威斯康星州麦迪逊的Paul BarfordU这个项目解决了从多个监测点检测和分类空间分布的网络异常的长期而困难的问题。要描述互联网的基线行为和异常行为，需要为复杂的大型系统部署协作数据收集、异常检测和模式识别。该项目汇集了三个相辅相成的学科的主要研究人员的力量：(1)联网和数据收集；(2)统计数据分析和信号处理；(3)分散决策。这项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，正在开发工具和实用的数据共享算法，用于检测分散的网络(如互联网)中的协同入侵、分布式拒绝服务攻击和服务质量下降。该项目还包括具有更广泛影响的活动，包括：创建公共网络异常数据库、K-12教育推广和大学与行业合作。研究方法基于模块化和分布式监测范例，该范例被组织为三个层次：来自服务器、路由器和交换机的本地级别的数据测量；中间级别的数据分析和处理从本地级别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级别传输的信息。这种模块化结构可扩展到大型监测点网络。然而，这种结构也对数据分析施加了限制，这需要开发新的方法。目前正在采用三种方法：使用图形上的小波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；以及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。

ABSTRACT0325653Paul BarfordU of Wisconsin Madison This 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 largescale 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.