CCRI: NEW: CLASSNET: Community Labeling and Sharing of Security and Networking Test datasets

CCRI：新：CLASSNET：安全和网络测试数据集的社区标签和共享

• 批准号: 2120400
• 负责人: Jelena Mirkovic
• 金额: $ 180万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120400&HistoricalAwards=false
• 关键词: CCRI; NEW; CLASSNET; Community; Labeling

The Community Labeling and Sharing of Security and Networking Test datasets (CLASSNET) project will provide new, labeled, rich and diverse datasets to the research community to support network and security research. The project will develop a framework for collaborative, community-driven enrichment and labeling of data, enabling use of these datasets for machine learning (ML) in networking and security. Furthermore, the CLASSNET project will make data available to researchers through multiple methods, ensuring privacy of data while enabling flexible data computation. The project will also generate diverse continuous (constantly, automatically updated) and curated (selected by human) datasets for research use.CLASSNET project will innovate in dimensions of data labeling, data distribution and data sources. In data labeling, the CLASSNET collaborative framework will provide a low-friction framework for sharing annotations among researchers. The framework will incentivize labeling with feedback mechanisms and user credits, and support bulk, automatic, algorithmic labeling. In data distribution, CLASSNET will support multiple ways of data access, ranging from downloading anonymized data to processing data in the cloud, on provider machines or via the code-to-data approach. Finally, CLASSNET data sources will provide new, diverse, continuous, and curated datasets that are useful for network and security research, including traffic packets and flows, network telescope data, Domain Name System (DNS) data and Internet topology data.The immediate impact of this project will include new types of labeled, curated and continuous datasets that enable new security, networking, and ML research and education, impacting a large community. The broader impact of this data will be to foster research and education, which will make the Internet safer, more stable, and more secure, and will increase the community's knowledge about the Internet. With the Internet's importance for tele-work, tele-medicine, remote learning, e-commerce and e-government, these improvements will have a broad societal impact. In addition, CLASSNET datasets will support data-driven exercises for graduate and undergraduate education, and new PhD research. CLASSNET project's innovations in multiple pathways to data access, combined with The automated and incentivized enrichment framework, will improve the state-of-the-art for responsible data sharing in related disciplines of information technology.The project website will be https://ant.isi.edu/classnet/. The website will remain operational after the project completes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

安全和网络测试数据集（ClassNet）项目的社区标签和共享项目将为研究社区提供新的，标签，丰富和多样化的数据集，以支持网络和安全研究。该项目将开发一个框架，用于协作，社区驱动的数据的数据，并在网络和安全性中使用这些数据集（ML）。此外，ClassNet项目将通过多种方法向研究人员提供数据，从而确保数据的隐私，同时启用灵活的数据计算。该项目还将生成多样化的连续（不断自动更新）并策划（由人类选择）用于研究使用的数据集。ClassNetProject将在数据标记，数据分布和数据源的维度上进行创新。在数据标签中，ClassNet协作框架将为研究人员共享注释提供一个低摩擦框架。该框架将通过反馈机制和用户信用来激励标签，并支持散装，自动，算法标签。在数据分发中，ClassNet将支持多种数据访问方式，从下载匿名数据到在云中，在提供商机器上或通过代码到数据的方法处理数据。最后，ClassNet数据源将提供新的，多样化的，连续的和精心策划的数据集，这些数据集可用于网络和安全研究，包括流量数据包和流，网络望远镜数据，域名系统（DNS）数据和Internet拓扑数据。该项目的直接影响将包括新的标记，策划和连续数据的新型安全性，并启用了新的安全性，并启用了新的安全性，并启用了新的安全性，并启用了New newertect，以及MM研究和ML研究。这些数据的更广泛影响将是培养研究和教育，这将使互联网更安全，更稳定，更安全，并将增加社区对互联网的了解。由于互联网对电视工作，远程学习，电子商务和电子政务的重要性，这些改进将产生广泛的社会影响。此外，ClassNet数据集将支持研究生和本科教育的数据驱动练习以及新的博士研究。 ClassNet Project在数据访问的多种途径中的创新，结合自动化和激励性的丰富框架，将改善信息技术相关学科中负责任的数据共享的最先进。项目完成后，该网站将继续运营。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准评估被认为值得支持。

项目成果

Cloud Watching: Understanding Attacks Against Cloud-Hosted Services

• DOI: 10.1145/3618257.3624818
• 发表时间: 2023-09
• 期刊: Proceedings of the 2023 ACM on Internet Measurement Conference
• 作者: Liz Izhikevich;M. Tran;Michalis Kallitsis;Aurore Fass;Zakir Durumeric

Inferring Changes in Daily Human Activity from Internet Response

从互联网响应推断人类日常活动的变化

• DOI: 10.1145/3618257.3624796
• 发表时间: 2023
• 期刊: ACM
• 作者: Song, Xiao;Baltra, Guillermo;Heidemann, John
• 通讯作者: Heidemann, John

Detecting and Interpreting Changes in Scanning Behavior in Large Network Telescopes

• DOI: 10.1109/tifs.2022.3211644
• 发表时间: 2022
• 期刊: IEEE Transactions on Information Forensics and Security
• 影响因子: 6.8
• 作者: Michalis Kallitsis;Rupesh Prajapati;Vasant G Honavar;Dinghao Wu

How to Operate a Meta-Telescope in your Spare Time

如何在业余时间操作超望远镜

• 发表时间: 2023
• 期刊: Internet Measurement Conference
• 作者: D. Wagner, S. Ranadive

Aggressive Internet-Wide Scanners: Network Impact and Longitudinal Characterizatio

激进的互联网范围扫描器：网络影响和纵向特征

• 发表时间: 2023
• 期刊: ACM 19th International Conference on emerging Networking EXperiments and Technologies
• 作者: Aniket Anand, Michalis Kallitsis