Collaborative Research: Implementation: Medium: Secure, Resilient Cyber-Physical Energy System Workforce Pathways via Data-Centric, Hardware-in-the-Loop Training

协作研究：实施：中：通过以数据为中心的硬件在环培训实现安全、有弹性的网络物理能源系统劳动力路径

• 批准号: 2320973
• 负责人: Zhuo Lu
• 金额: $ 38.5万
• 依托单位: University of South Florida
• 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: 2023-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320973&HistoricalAwards=false
• 关键词: Collaborative; Research; Implementation; Medium; Secure

This project will establish a CyberTraining hub of cyber physical energy systems in Southern Florida, a region that suffers from numerous coastal processes impacting critical infrastructures and therefore would greatly benefit from resilience and security research and education. This project aims to provide students and researchers with mentored, hands-on training. The faculty team has expertise across power engineering, communication networks, data science, and cyber/hardware security to build a novel model for energy cyberinfrastructure resilience education. The curriculum and instructional materials to be developed will bring advanced skills from multiple areas, under the umbrella of cyber-physical energy systems. Participants will develop and refine multi-disciplinary skillsets needed for the data-centric energy industry, using the unique, remotely connected smart grid cyberinfrastructure. Participants will extend their academic research portfolios, strengthening their career competitiveness as future cyberinfrastructure professionals and users. Two-week workshops will immerse undergraduate/graduate students and researchers in a unique training opportunity through laboratory demonstrations and mini workshops. Semester-long projects will provide research-intensive training and further strengthen the participants' real-world problem-solving capabilities. The project will tackle three technical challenges with significant intellectual merit: (1) Curating comprehensive, heterogeneous benign and malicious cyber-physical data from a remotely-connected cyberinfrastructure platform to strongly enable hands-on training and research. Hardware-in-the-loop power testbed connected with a virtual network laboratory will be used to characterize system dynamics, where state-of-the-art hardware and software modules will enable humans, machines, and grids to cooperate in a near-to-real learning environment; (2) Developing user-friendly cybersecurity modules in a virtual lab to simulate threats from cyberspace, enabling self-paced training. Participants will create attack scenarios in the cyber domain, where the attacking process and consequence are visualized in the physical system. Data analysis techniques based on machine learning (ML)/artificial intelligence (AI) will be developed as a set of cyber defense mechanisms; (3) Designing, delivering, and integrating cross-disciplinary curricula, composed of undergraduate and graduate course modules and a certificate program. The project further offers unique training opportunities with relevant private and public sector partners through capstone projects and internships.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.

该项目将在佛罗里达州南部建立一个网络物理能源系统的网络培训中心，该地区遭受众多影响关键基础设施的沿海过程，因此将极大地受益于弹性和安全研究和教育。该项目旨在为学生和研究人员提供指导性的实践培训。教师团队拥有电力工程、通信网络、数据科学和网络/硬件安全方面的专业知识，可以构建能源网络基础设施弹性教育的新模型。待开发的课程和教学材料将在网络物理能源系统的保护下带来来自多个领域的先进技能。参与者将利用独特的远程连接智能电网网络基础设施，开发和完善以数据为中心的能源行业所需的多学科技能。参与者将扩展他们的学术研究组合，增强他们作为未来网络基础设施专业人员和用户的职业竞争力。为期两周的研讨会将通过实验室演示和小型研讨会让本科生/研究生和研究人员沉浸在独特的培训机会中。为期一个学期的项目将提供研究密集型培训，并进一步增强参与者解决实际问题的能力。该项目将解决三个具有重大智力价值的技术挑战：（1）从远程连接的网络基础设施平台管理全面的、异构的良性和恶意的网络物理数据，以有力地实现实践培训和研究。与虚拟网络实验室连接的硬件在环功率测试台将用于表征系统动力学，其中最先进的硬件和软件模块将使人类、机器和电网能够以近乎完美的方式进行合作。 -真实的学习环境； (2) 在虚拟实验室中开发用户友好的网络安全模块，以模拟来自网络空间的威胁，从而实现自定进度的培训。参与者将在网络领域创建攻击场景，其中攻击过程和结果在物理系统中可视化。基于机器学习（ML）/人工智能（AI）的数据分析技术将被开发为一套网络防御机制； (3) 设计、交付和整合跨学科课程，由本科生和研究生课程模块以及证书课程组成。该项目还通过顶点项目和实习为相关私营和公共部门合作伙伴提供独特的培训机会。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。