Collaborative Research: Research Infrastructure: CCRI:New: Data-Driven Cybersecurity Research Infrastructure for Smart Manufacturing

合作研究：研究基础设施：CCRI：新：数据驱动的智能制造网络安全研究基础设施

• 批准号: 2234975
• 负责人: sidi berri
• 金额: $ 2.82万
• 依托单位: CUNY New York City College of Technology
• 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: 2023-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234975&HistoricalAwards=false
• 关键词: Collaborative; Research; Infrastructure; CCRI; New

Recent advances in Internet of Things sensors, artificial intelligence, computing, and communications are enabling a more distributed manufacturing paradigm where custom products are made at the point and time of need using smart manufacturing (SM) technologies. While connectivity forms the backbone in realizing SM, it has opened new cybersecurity risks and challenges, requiring a fundamental rethink and foundational efforts to tackle these challenges. Recent National Science Foundation-sponsored workshops have underscored a compelling need for a research infrastructure that can bridge the traditional divide between manufacturing and cybersecurity professionals and foster a community of CISE experts in manufacturing cybersecurity. Creating such an infrastructure is essential to understand the cybersecurity vulnerabilities, and to develop solutions tailored to SM systems. This project envisions the creation of a vibrant CISE research community for data-driven cybersecurity for SM by launching a community research platform integrating three elements: (1) A web infrastructure that enables virtual “playgrounds” to share data, codes, resources, and tools, mostly contributed by the research community; (2) a data infrastructure for curating diverse cybersecurity datasets; and (3) a SM machine infrastructure that will leverage established national resources, including the Smart Manufacturing Innovation Platform from the Department of Energy. Taken together, this infrastructure will help CISE researchers to collaborate with the SM community –spanning academia, industry, and government– to create and share data for understanding the vulnerabilities of a network of SM machines and other components of a manufacturing enterprise, and facilitate the development of AI-driven cybersecurity innovations for SM. This infrastructure will enable CISE-relevant, data-driven research thrusts spanning five key SM system elements: (1) Cybersecurity by design; (2) compiler optimizations for SM process plans and toolpaths; (3) forensics and watermarking of SM machines; (4) protections against SM process-unique side-channels; and (5) fingerprinting and securing SM networks. The community will be able to contribute their SM equipment, relevant hardware and software components, data, and innovations to grow this networked cyber-physical platform infrastructure. Emerging manufacturing enterprises are increasingly adopting advanced sensor and AI technologies and transforming themselves into smart manufacturing systems. These smart systems can connect multiple manufacturers, designers, and businesses to efficiently meet customer demands. A SM network is noted by IBM as the largest target for cybersecurity attacks among all industry sectors. Securing such a network is a daunting task. This project aims to create an online resource consisting of web infrastructure, data, and machines to bring together professionals from the manufacturing and cybersecurity communities. This infrastructure will enhance collaborations among these communities to enable a deeper understanding of current and emerging cybersecurity threats to smart manufacturing environments and enable the development of innovations to assure cybersecurity within the smart manufacturing sector. It will particularly benefit diverse researchers, practitioners, and students from cybersecurity, AI, and manufacturing disciplines. For example, the project will leverage the Inclusive Engineering Consortium, a network of 20 Underrepresented Minority serving engineering colleges to enable broad-based impact.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.

物联网传感器，人工智能，计算和通信的最新进展正在实现一个更具分布的制造范式，其中使用智能制造（SM）技术在需要的时候和时间制造自定义产品。尽管连通性构成了实现SM的骨干，但它开辟了新的网络安全风险和挑战，需要重新考虑解决这些挑战的基本思维和基本努力。最近，国家科学基金会赞助的研讨会未列出了对研究基础设施的迫切需求，该研究基础设施可以弥合制造业和网络安全专业人员之间的传统鸿沟，并培养了CISE在制造网络安全方面的CISE专家社区。建立这样的基础架构对于了解网络安全漏洞并开发针对SM系统量身定制的解决方案至关重要。该项目设想通过启动一个集成了三个要素的社区研究平台来创建一个充满活力的CISE研究社区，用于数据驱动的SM的网络安全：（1）一个Web基础架构，该网络基础架构使虚拟“ Playgrounds”可以共享数据，代码，资源和工具，大多数由研究社区贡献； （2）用于策划潜水员网络安全数据集的数据基础架构； （3）SM机器基础设施将利用已建立的国家资源，包括能源部的智能制造创新平台。综上所述，该基础架构将帮助CISE研究人员与SM社区（跨越学术界，行业和政府）合作创建和共享数据，以了解SM机器网络和制造企业的其他组件的脆弱性，并支持SM驱动的网络安全创新的SM SM驱动网络安全创新。该基础架构将使与五个关键SM系统元素相关的，数据驱动的研究推力：（1）通过设计网络安全； （2）用于SM流程计划和工具路径的编译器优化； （3）SM机器的取证和水印； （4）防止SM过程唯一侧通道的保护； （5）指纹和保护SM网络。社区将能够为他们的SM设备，相关硬件和软件组件，数据和创新提供贡献，以增强这种网络网络物理平台基础架构。新兴的制造企业越来越多地适应高级传感器和AI技术，并将自己转变为智能制造系统。这些智能系统可以连接多个制造商，设计师和企业，以有效地满足客户需求。 IBM指出，SM网络是所有行业领域网络安全攻击的最大目标。确保这样的网络是一项艰巨的任务。该项目旨在创建一个由Web基础架构，数据和机器组成的在线资源，以将制造业和网络安全社区的专业人员聚集在一起。这种基础设施将增强这些社区之间的合作，以使对当前和新兴的网络安全威胁对智能制造环境的威胁有了更深入的了解，并使创新的开发能够确保智能制造业内的网络安全。它将特别受益于网络安全，AI和制造学科的潜水员研究人员，从业人员和学生。例如，该项目将利用包容性的工程联盟，这是一个由20个代表性不足的工程学院组成的网络，以实现广泛的影响。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优点和更广泛的影响标准通过评估来获得的支持。