Collaborative Research: CyberTraining: Pilot: Interdisciplinary Training of Data-Centric Security and Resilience of Cyber-Physical Energy Infrastructures

合作研究：网络培训：试点：以数据为中心的网络物理能源基础设施安全性和弹性的跨学科培训

• 批准号: 2017194
• 负责人: Zhuo Lu
• 金额: $ 14万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017194&HistoricalAwards=false
• 关键词: Collaborative; Research; CyberTraining; Pilot; Interdisciplinary

In this project, students and researchers are provided with mentored, hands-on training combining expertise across electrical engineering, communication, science and technology studies, and data science. This establishes a novel model for energy cyberinfrastructure resilience education. The curriculum and instructional materials that are developed integrate advanced skills from multiple areas under the umbrella of cyber-physical energy systems. Participants develop and refine the multi-disciplinary skillsets needed for the data-centric energy industry using unique, remotely connected smart grid cyberinfrastructure. Participants extend their academic research portfolios, strengthening their career competitiveness as future cyberinfrastructure professionals and users. The two-week workshop immerses undergraduate/graduate students and research scientists in a unique training opportunity through laboratory demonstrations and mini projects. Three main technical challenges are tackled in the project: (i) Establishment of a new remotely connected cyberinfrastructure platform among the collaborating universities. An existing hardware-in-the-loop power testbed using a real-time digital simulator is connected with a virtual network laboratory to characterize cyber-physical energy systems. The combined infrastructure is equipped with state-of-the-art hardware and software modules, where humans, machines, and power girds can interact and cooperate in a near-to-real learning environment. (ii) Implementation of a cybersecurity module in the virtual lab to simulate cyber threats, such as denial-of-service attacks and man-in-the-middle attacks. Participants create attack scenarios that are played out, where the attacking process and consequence can be visualized on the physical system. (iii) Development of data analytics techniques based on machine learning as a set of cyber defense mechanisms, such as data-driven adversarial event detection. Participants execute their cyber defense algorithms alongside the attack, and the effectiveness of these defenses is validated and visualized using the testbed.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.

在该项目中，学生和研究人员将获得结合电气工程、通信、科学技术研究和数据科学专业知识的指导性实践培训。 这为能源网络基础设施弹性教育建立了一种新颖的模式。开发的课程和教学材料整合了网络物理能源系统下多个领域的高级技能。参与者使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。参与者扩展了他们的学术研究组合，增强了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。为期两周的研讨会通过实验室演示和小型项目，让本科生/研究生和研究科学家沉浸在独特的培训机会中。该项目解决了三个主要技术挑战：（i）在合作大学之间建立一个新的远程连接的网络基础设施平台。使用实时数字模拟器的现有硬件在环电力测试台与虚拟网络实验室连接，以表征网络物理能源系统。组合基础设施配备了最先进的硬件和软件模块，人、机器和电网可以在近乎真实的学习环境中交互和合作。 (ii) 在虚拟实验室中实施网络安全模块，以模拟网络威胁，例如拒绝服务攻击和中间人攻击。参与者创建要执行的攻击场景，其中攻击过程和结果可以在物理系统上可视化。 (iii) 开发基于机器学习的数据分析技术作为一套网络防御机制，例如数据驱动的对抗事件检测。参与者在攻击的同时执行网络防御算法，并使用测试台验证和可视化这些防御的有效性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。