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REU Site: Secure and Privacy-Preserving Cyber-Physical Systems: Software and Hardware Approaches

REU Site: Secure and Privacy-Preserving Cyber-Physical Systems: Software and Hardware Approaches
REU 网站:安全和隐私保护的网络物理系统:软件和硬件方法
批准号:
1852126
负责人:
Mohamed Mahmoud
金额:
$36.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-02-01 至 2023-01-31

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英文摘要
This Research Experiences for Undergraduates (REU) Site award funds a renewal site at Tennessee Technological University focused on preparing future researchers who can protect the nation's critical infrastructure. The site's focus is on Cyber-Physical Systems (CPSs), a class of engineering systems where computation and communication network are used to monitor and control physical components. CPSs play a major role in the design and development of modern systems and initiatives, such as smart and safe cities, self-driving cars, industrial internet of things (IIoT), and smart power grid. Security breaches in CPSs can occur through software or hardware. Either one can halt critical infrastructures and reveal citizens' locations and activities, making the security of CPSs crucial for U.S. national security.An expert group of faculty members will mentor these REU participants to conceive, design, implement, and assess research projects in the areas related to security and privacy in CPSs. The team plans to conduct nationwide recruitment efforts in partnership with Minority Serving Institutions, and to reach out to meritorious students from under-represented communities, women, students with disabilities and from institutions limited in Science, Technology, Engineering and Math (STEM) research. The site will investigate both software and hardware intrinsic attacks in CPS. Undergraduate students will have the opportunity to learn diverse topics including, Blockchain for securing CPS, challenges with hardware and software attacks on IIoT, threats of adversarial learning attacks on the deployment of deep learning architectures on resource constraint hardware. Students will get exposure to different toolsets in domains such as cryptography, graph-based anomaly detection, and convolutional neural networks.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.
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