II-New: A Dual-Purpose Data Analytics Laboratory

支持基础设施的研究有可能在两个方面对社会产生巨大影响。首先，通过破坏整个网络犯罪生态系统：通过战略干预破坏地下活动、基础设施和社会网络。抑制资金流动降低了这些活动的盈利能力，从而颠覆了现代网络犯罪的主要动机。第二，数据中心网络效率的提高将显著降低运营成本，提高能源效率。该基础设施还将为不同层次的学生创造教育机会，扩大博士后、研究生和本科生的研究技能，以应对数据中心网络设计和安全研究方面的挑战。该项目将进行两个独立的多年研究议程。一是收集和分析与互联网恶意软件和网络犯罪的各个方面有关的超大数据集，同时探索新的高性能混合光/电网络架构，从而大大降低支持此类分析所需的基础设施的成本和复杂性。该合同支持计算和存储资源，以进行Ecrime研究所需的分析，同时作为我们原型混合网络交换机的测试平台。该基础设施支持的研究有两个关键组成部分：1)通过对一系列在线犯罪活动的深入实证分析，pi正在发展对关键经济和社会力量形态的理解——从规模上看——就攻击者和防御者的相关性而言。2)描述由大规模数据分析产生的网络流量，特别侧重于识别电路交换机可以支持的网络流量类别，以及网络中电路和分组部分之间的流量划分。

The research enabled by the supported infrastructure has the potential to dramatically impact society in two ways. First, by undermining entire cyber-crime ecosystems: disrupting underground activities, infrastructure, and social networks through strategic intervention. Inhibiting the flow of money reduces the profitability of these activities, thereby subverting the key incentive underlying modern cybercrime. Second, improved efficiency of data center networks will significantly reduce operating costs and increase energy efficiency. The infrastructure will also create educational opportunities for students at a variety of levels, expanding the research skills of postdoc, graduate, and undergraduate students to address both data center network design and security research challenges.This project is to pursue two separate multi-year research agendas. One is to collect and analyze extremely large datasets pertaining to various aspects of Internet malware and cybercrime while concurrently exploring new high-performance hybrid optical/electrical network architectures that dramatically decrease the cost and complexity of the infrastructure required to support such analytics. This award supports compute and storage resources to both conduct the analytics required for the Ecrime research, while simultaneously serving as a testbed for our prototype hybrid network switches.The research enabled by this infrastructure has two key components: 1) Through in-depth empirical analyses of a range of online criminal activities, the PIs are developing an understanding of the shape of key economic and social forces---as seen at scale---in terms of relevance for both attackers and defenders. 2) Characterizing network traffic generated by large-scale data analytics, focusing specifically on identifying the class of network traffic that a circuit switch can support as well as the partitioning of the traffic between the circuit and packet portions of the network.