CICI: UCSS: Human-Centered Cybersecurity in Robotic Surgery (HCCRS) - Coordinating the Human and Cyber Infrastructure for Cybersecurity

CICI：UCCSS：机器人手术中以人为中心的网络安全 (HCCCS) - 协调网络安全的人力和网络基础设施

• 批准号: 2319891
• 负责人: Jackie Cha
• 金额: $ 60万
• 依托单位: Clemson University
• 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: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319891&HistoricalAwards=false
• 关键词: CICI; UCSS; Human; Centered; Cybersecurity

Robotic-assisted surgery cyberinfrastructure combines advanced data analytics and decision making in the cloud with interconnected Internet of Things-based systems that has brought opportunities for advanced diagnostics, patient monitoring, and personalized medicine. However, the increased data and resource sharing enabled by these devices and artificial intelligence in the robotic-assisted surgery infrastructure has increased vulnerabilities and opportunities for cyberattacks. In particular, data-integrity attacks that interfere with critical real-time data can potentially disrupt surgical performance and compromise patient safety. Existing studies on cyberinfrastructure security in these environments do not fully consider users’ cybersecurity awareness and knowledge or evaluate training and mitigation for cyberattacks. This project aims to create a Human-Centered Cybersecurity in Robotic Surgery (HCCRS) framework by integrating data analytics and healthcare human factors to design new human-centered algorithms to detect, identify, and mitigate cyberattacks in the robotic surgery cyberinfrastructure. The HCCRS framework will lead to sustained impact that can be translated to other digital health technologies and generalized to the National Initiative for Cybersecurity Education Cybersecurity Workforce Framework. This work addresses the critical gap in cybersecurity for surgical care. Specifically, the following research aims are studied: (1) identify robotic surgery cybersecurity perspectives of stakeholders through qualitative research approaches; (2) design a data-driven human-centered attack detection and identification framework using Graphical Neural Networks-based impact quantification and multimodal high-dimensional data fusion techniques; and (3) develop and validate robotic surgery cybersecurity training materials and an cyberattack intervention system. This work enhances the performance and usability of current and future heterogenous robotic surgery cyberinfrastructures.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.

机器人辅助手术网络基础设施将云端的高级数据分析和决策与基于物联网的互联系统相结合，为先进的诊断、患者监测和个性化医疗带来了机会。然而，这些设备和机器人辅助手术基础设施中的人工智能实现的数据和资源共享增加了网络攻击的脆弱性和机会。特别是，干扰关键实时数据的数据完整性攻击可能会破坏手术性能并危及患者安全。关于这些环境中网络基础设施安全的现有研究没有充分考虑用户的网络安全意识和知识，也没有评估网络攻击的培训和缓解措施。该项目旨在通过整合数据分析和医疗保健人为因素来创建以人为本的机器人手术网络安全（HCCRS）框架，以设计新的以人为本的算法来检测，识别和减轻机器人手术网络基础设施中的网络攻击。HCCRS框架将产生持续的影响，可以转化为其他数字健康技术，并推广到国家网络安全教育倡议网络安全劳动力框架。这项工作解决了外科护理网络安全方面的关键差距。具体而言，研究目标如下：（1）通过定性研究方法识别利益相关者对机器人手术网络安全的观点;（2）使用基于图形神经网络的影响量化和多模态高维数据融合技术，设计一个数据驱动的以人为中心的攻击检测和识别框架;以及（3）开发和验证机器人手术网络安全培训材料和网络攻击干预系统。这项工作提高了当前和未来异构机器人手术网络基础设施的性能和可用性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。