CICI:TCR:STARNOVA: Scalable Technology to Accelerate Research Network Operations Vulnerability Alerts

CICI:TCR:STARNOVA：可扩展技术加速研究网络运营漏洞警报

• 批准号: 2319959
• 负责人: Ka Pui Mok
• 金额: $ 120万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319959&HistoricalAwards=false
• 关键词: CICI; TCR; STARNOVA; Scalable; Technology; CICI; TCR; STARNOVA; Scalable; Technology

Cyberinfrastructure plays a crucial role in performing computational and data-intensive scientific tasks across various disciplines, including data science, physics, bioinformatics, health IT, and Internet analytics. Targeted cyber attacks, such as ransomware, malware, and denial-of-service attacks, can severely impact the infrastructure’s security, reliability, and robustness, leading to significant disruptions to scientific workflows. Timely and customized threat intelligence facilitates development of strategies for network operators defending against emerging attacks.This project designs and implements the Sustainable Technology to Accelerate Research Network Operations Vulnerability Alerts (STARNOVA) platform. STARNOVA leverages unsolicited Internet traffic to provide early-warning indicators of new attacks targeted to cyberinfrastructure at the San Diego Supercomputer Center (SDSC). The newly developed infrastructure extends the visibility of the UCSD network telescope to monitor unsolicited traffic toward SDSC's equipment IP address spaces. The platform applies novel machine learning-based time series analytics methods to detect anomalies in traffic patterns. The analysis results of each anomaly will automatically generate near real-time alerts for SDSC operators to make informed defensive actions against potential threats. The published software and results from this project facilitates traffic data collection and information exchange across the network security and scientific CI community, contributing to affordable community-based solutions, especially critical at under-resourced institutions.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.

网络基础设施在各种学科的计算和数据密集的科学任务中起着至关重要的作用，包括数据科学，物理学，生物信息学，健康IT和Internet Analytics。有针对性的网络攻击，例如勒索软件，恶意软件和拒绝服务攻击，可能会严重影响基础架构的安全性，可靠性和鲁棒性，从而导致对科学工作流的严重破坏。及时，定制的威胁情报设施为网络运营商的策略制定防御新兴攻击。该项目设计并实施可持续技术，以加速研究网络操作漏洞警报（Starnova）平台。 STARNOVA利用未经请求的Internet流量为圣地亚哥超级计算机中心（SDSC）的针对网络基础设施的新攻击提供了早期训练指标。新开发的基础架构扩展了UCSD网络望远镜的可见性，以监视SDSC设备IP地址空间的主动型流量。该平台应用了基于机器学习的新型时间序列分析方法来检测交通模式的异常情况。每个异常的分析结果将自动为SDSC操作员提供接近实时的警报，以对潜在威胁采取明智的防御行动。该项目设施的已发表的软件以及整个网络安全和科学CI社区的流量数据收集和信息交流的结果，为负担得起的基于社区的解决方案做出了贡献，尤其是在资源不足的机构中至关重要的。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响审查Criteria评估来通过评估来获得的支持。