REU Site: Secure, Robust, and Resilient AI-enabled System Engineering

REU 站点：安全、稳健且有弹性的人工智能系统工程

• 批准号: 2050972
• 负责人: Ming Shao
• 金额: $ 40.46万
• 依托单位: University of Massachusetts, Dartmouth
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050972&HistoricalAwards=false
• 关键词: REU; Site; Secure; Robust; Resilient

Artificial intelligence (AI) has gained significant attention in recent years as an enabler of technologies such as autonomous cyber-physical systems and cybersecurity. This funding establishes a new Research Experiences for Undergraduates (REU) Site at the University of Massachusetts Dartmouth with a focus on secure, robust, and resilient AI-enabled system engineering. In each summer, ten undergraduate students will participate in research for ten weeks. During the research, participants will learn engineering principles to support the rigorous design and quantitative test of systems and incorporate AI components to ensure properties such as security, robustness, and resilience. This program will provide a research experience with a balance of theory, hands-on practical skills, and applications. The project is led by a team of experienced faculty who will mentor the participating researchers through a number of professional development activities, which prepare them to take a systems perspective while maintaining technical excellence to enhance the robustness of AI-enabled systems in commercial and societal contexts. This project will provide unique opportunities for undergraduate students, especially for those from underrepresented and minority groups and institutions with limited research resources, to participate in real-world research.Artificial intelligence has gained significant attention in recent years as an enabler of technologies such as autonomous cyber physical systems and cyber security. However, engineering principles to support the rigorous design and quantitative test of systems incorporating AI components are needed to ensure properties such as security, robustness, and resilience. AI system engineering research will provide the foundation required to establish user trust and promote regulatory activities such as safety assurance. To enable to the promise of AI systems, this project proposes a unique Research Experiences for Undergraduates (REU) Site focused on secure, robust, and resilient AI-enabled system engineering. This REU Site will intertwine AI research with cybersecurity and systems engineering in a portfolio of synergistic projects. Ten undergraduate students will be engaged in a ten-week summer research program to conduct research at the University of Massachusetts Dartmouth (UMD). The themes of the proposed REU Site are AI, cybersecurity, and system engineering research and their combined implications. Specifically, the following research topics will be explored: (1) cyber attacks that degrade the performance of AI algorithms; (2) AI-enabled cybersecurity against malicious actors; (3) resilient AI-enabled system engineering methods. These research themes will create opportunities for multidisciplinary knowledge sharing between AI and cybersecurity from software, hardware and system engineering perspectives, including topics in biometrics, UAVs, brain computer interaction, and signal processing. The REU will prepare talented young researchers to take a systems perspective, while maintaining technical excellence to enhance the robustness of AI-enabled systems in commercial and societal contexts. The site will provide unique opportunities for undergraduate students, especially for those from underrepresented and minority groups and institutions with limited research resources, to participate in real-world research.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.

近年来，作为自主网络物理系统和网络安全等技术的推动力，人工智能（AI）已引起了人们的关注。这笔资金为马萨诸塞州达特茅斯大学的本科生（REU）网站建立了新的研究经验，重点是安全，健壮且具有弹性的AI-AI-Systable系统工程。每个夏天，十个本科生将参加十个星期。在研究期间，参与者将学习工程原理，以支持系统的严格设计和定量测试，并结合AI组件，以确保诸如安全性，鲁棒性和弹性等属性。该计划将以理论，实践技能和应用的平衡提供研究经验。该项目由一支经验丰富的教师团队领导，他们将通过许多专业发展活动来指导参与的研究人员，这使他们能够采取系统的观点，同时保持技术卓越，以增强AI-ai-abele Systems在商业和社会环境中的鲁棒性。该项目将为本科生提供独特的机会，尤其是对于那些人数不足和少数群体和有限研究资源的机构的人，可以参与现实世界研究。近年来，人工智能已引起了人们的重大关注网络物理系统和网络安全。但是，需要工程原则以支持合并AI组件的系统的严格设计和定量测试，以确保诸如安全性，鲁棒性和弹性等属性。 AI系统设计研究将为建立用户信任和促进监管活动（例如安全保证）提供基础。为了实现AI系统的承诺，该项目为本科生（REU）网站提出了一个独特的研究经验，该网站着重于安全，健壮和弹性的AI-AI-System System Engineering。此REU站点将在协同项目的投资组合中将AI研究与网络安全和系统工程相结合。十个本科生将参加为期十周的夏季研究计划，以在马萨诸塞大学达特茅斯大学（UMD）进行研究。 拟议的REU站点的主题是AI，网络安全和系统设计研究及其综合含义。具体而言，将探讨以下研究主题：（1）降低AI算法性能的网络攻击； （2）针对恶意演员的AI支持的网络安全； （3）弹性AI支持的系统工程方法。这些研究主题将为通过软件，硬件和系统工程的观点（包括生物识别技术，无人机，大脑计算机互动和信号处理）提供了AI和网络安全之间的多学科知识共享的机会。 REU将为有才华的年轻研究人员做好准备，以采取系统的观点，同时保持技术卓越，以增强商业和社会环境中AI支持系统的稳健性。该网站将为本科生提供独特的机会，尤其是对于那些人数不足的人和少数群体和有限研究资源的机构的机构，可以参与现实世界的研究。该奖项反映了NSF的法定任务，并被认为是值得通过使用评估的人来支持的。基金会的智力优点和更广泛的影响评论标准。

项目成果

A Novel Convolutional Neural Network for Emotion Recognition Using Neurophysiological Signals

• DOI: 10.1109/icra46639.2022.9811868
• 发表时间: 2022-05
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Marc Tunnell;Huijin Chung;Yuchou Chang

Sensor Data-Driven UAV Anomaly Detection using Deep Learning Approach

• DOI: 10.1109/milcom52596.2021.9653036
• 发表时间: 2021-11
• 期刊: MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)
• 作者: Julio Galvan;A. Raja;Yanyan Li;Jiawei Yuan

Machine-Learning PUF-based Detection of RF Anomalies in a Cluttered RF Environment

杂乱 RF 环境中基于机器学习 PUF 的 RF 异常检测

• DOI: 10.1109/hst53381.2021.9619834
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Technologies for Homeland Security (HST
• 作者: Lu, James;Morehouse, Todd;Yuan, Jiawei;Zhou, Ruolin
• 通讯作者: Zhou, Ruolin

Predictive Resilience Modeling

• DOI: 10.1109/rws55399.2022.9984025
• 发表时间: 2022-09
• 期刊: 2022 Resilience Week (RWS)
• 作者: Priscila Silva;Mariana Hermosillo Hidalgo;I. Linkov;L. Fiondella

Adversary on Multimodal BCI-based Classification

基于多模式 BCI 分类的对手

• 发表时间: 2023
• 期刊: 11th International IEEE EMBS Conference on Neural Engineering
• 作者: Kumar, Chetan;Donohue, James P.;Gonjari, Rohan;Rahimi, Neela;McLinden, John;Shahriari, Yalda;Shao, Ming
• 通讯作者: Shao, Ming