CI-SUSTAIN: Sustainable Tools for Analysis and Research on Darknet Unsolicited Traffic (STARDUST).

CI-SUSTAIN：用于分析和研究暗网主动流量（STARDUST）的可持续工具。

• 批准号: 1730661
• 负责人: Alberto Dainotti
• 金额: $ 73.25万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1730661&HistoricalAwards=false
• 关键词: CI; SUSTAIN; Sustainable; Tools; Analysis

The Internet is a vast enterprise carrying information to people and devices all over the world. The University of California San Diego Network Telescope (UCSD-NT) is a unique monitoring system which, for over a decade, has enabled global visibility into Internet behavior that few other data sources can offer. It has provided relevant data used in a broad set of sub-disciplines in Computer & Information Science & Engineering (CISE) and beyond, from network and systems security and stability, to machine learning and big data processing techniques, and, most recently, for studies of cyberwarfare. The STARDUST project will help sustain operation and extend the applicability of the UCSD-NT infrastructure. The stabilized and enhanced infrastructure capabilities will serve a diverse range of academic researchers, the vast majority of whom have no access to any other source of global Internet traffic data. In addition, the proposed enhancements will support invaluable hands-on experience in operationally relevant network security and traffic analysis research for a wide audience of computer science students.STARDUST aims at maintaining continued operation of the UCSD-NT infrastructure while maximizing its utility to researchers from various disciplines. The scope of work includes upgrading and modernizing the current infrastructure to handle the predicted growth in traffic, leveraging virtualization and NSF-funded High-Performance Computing (HPC) platforms at the San Diego Supercomputer Center for computational data analysis, and introducing meta-data semantics to simplify many tasks researchers typically want to do with UCSD-NT data. The proposed modifications will leave researchers more time (and available HPC resources) to focus on their specific scientific questions. Moreover, the project will forge an interdisciplinary collaboration between researchers from the field of computer networks and HPC scientists and engineers to experiment with novel approaches for research on live traffic analysis. Project results will contribute to advancing knowledge in diverse CISE disciplines, e.g., facilitating the development of efficient strategies for early detection and mitigation of cyber attacks, supporting macroscopic Internet performance and reliability assessments, and opening a new domain for the application of live streaming big data analysis techniques.

互联网是一个向世界各地的人们和设备传递信息的庞大企业。加州大学圣地亚哥分校网络望远镜（UCSD-NT）是一个独特的监测系统，十多年来，它使全球互联网行为的可见性成为可能，这是其他数据源无法提供的。它为计算机与信息科学与工程（CISE）及其他领域的广泛子学科提供了相关数据，从网络和系统安全性与稳定性，到机器学习和大数据处理技术，以及最近的网络战研究。星尘项目将有助于维持UCSD-NT基础设施的运作和扩展其适用性。稳定和增强的基础设施能力将为各种学术研究人员提供服务，其中绝大多数人无法访问任何其他全球互联网流量数据来源。此外，拟议的增强功能将为广大计算机科学专业学生提供与操作相关的网络安全和流量分析研究方面的宝贵实践经验。STARDUST旨在维持UCSD-NT基础设施的持续运行，同时最大限度地提高其对不同学科研究人员的效用。工作范围包括升级和现代化当前的基础设施，以处理预计的流量增长，利用虚拟化和nsf资助的圣地亚哥超级计算机中心的高性能计算（HPC）平台进行计算数据分析，并引入元数据语义来简化研究人员通常希望对UCSD-NT数据进行的许多任务。拟议的修改将使研究人员有更多的时间（和可用的高性能计算资源）专注于他们具体的科学问题。此外，该项目将促成计算机网络领域的研究人员与高性能计算科学家和工程师之间的跨学科合作，以试验实时流量分析研究的新方法。项目成果将有助于推进CISE各学科的知识，例如，促进制定早期发现和缓解网络攻击的有效战略，支持宏观互联网性能和可靠性评估，并为实时流媒体大数据分析技术的应用开辟新的领域。

项目成果

At Home and Abroad: The Use of Denial-of-service Attacks during Elections in Nondemocratic Regimes

国内外：非民主政权选举期间拒绝服务攻击的使用

• DOI: 10.1177/0022002719861676
• 发表时间: 2019
• 期刊: Journal of Conflict Resolution
• 影响因子: 3.1
• 作者: Lutscher, Philipp M.;Weidmann, Nils B.;Roberts, Margaret E.;Jonker, Mattijs;King, Alistair;Dainotti, Alberto
• 通讯作者: Dainotti, Alberto

Inferring Carrier-Grade NAT Deployment in the Wild

推断运营商级 NAT 实际部署

• 发表时间: 2018
• 期刊: IEEE Conference on Computer Communications (INFOCOM
• 作者: Livadariu, I.;Benson, K.;Elmokashfi, A.;Dainotti, A.;Dhamdhere, A
• 通讯作者: Dhamdhere, A

On the Potential of BGP Flowspec for DDoS Mitigation at Two Sources: ISP and IXP

关于 BGP Flowspec 在两个来源（ISP 和 IXP）缓解 DDoS 方面的潜力

• DOI: 10.1145/3234200.3234209
• 发表时间: 2018
• 期刊: SIGCOMM '18 Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos
• 作者: Hinze, Nico;Nawrocki, Marcin;Jonker, Mattijs;Dainotti, Alberto;Schmidt, Thomas C.;Wählisch, Matthias
• 通讯作者: Wählisch, Matthias

Spatial Temporal Analysis of 40,000,000,000,000 Internet Darkspace Packets

40,000,000,000,000 个互联网暗空间数据包的时空分析

• DOI: 10.1109/hpec49654.2021.9622790
• 发表时间: 2021
• 期刊: 2021 IEEE High Performance Extreme Computing Conference (HPEC
• 作者: Kepner, Jeremy;Jones, Michael;Andersen, Daniel;Buluc, Aydin;Byun, Chansup;Claffy, K;Davis, Timothy;Arcand, William;Bernays, Jonathan;Bestor, David
• 通讯作者: Bestor, David

Hypersparse Neural Network Analysis of Large-Scale Internet Traffic

• DOI: 10.1109/hpec.2019.8916263
• 发表时间: 2019-04
• 期刊: 2019 IEEE High Performance Extreme Computing Conference (HPEC)
• 作者: J. Kepner;Kenjiro Cho;K. Claffy;V. Gadepally;P. Michaleas;Lauren Milechin