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REU Site: Research on Security of Mobile Devices and Wireless Networks at NYIT

REU 网站：纽约理工学院移动设备和无线网络安全研究

基本信息

批准号：
1852316
负责人：
Nabi Sertac Artan
金额：
$ 41.02万
依托单位：
New York Institute of Technology
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
起止时间：
2019-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1852316&HistoricalAwards=false
关键词：
REU Site Research Security Mobile

项目摘要

This Research Experiences for Undergraduates (REU) Site award funds the renewal of an outstanding REU site at New York Institute of Technology. The site will recruit undergraduates from across the nation to participate in research related to securing mobile devices such as smart phones and wireless networks. Securing mobile devices and networks is an area of current interest that is well-suited to undergraduate research productivity. The students will address the challenges of securing mobile devices and wireless networks by creating new biometrics for seamless and secure user authentication on mobile devices, developing power-efficient, privacy-preserving cryptographic protocols for secure wireless communications, detecting malware and identifying attacks of mobile devices to protect data integrity and privacy, and securing medical devices and data. This site should help develop a group of computing professionals who can design the systems of the future that impact society and enhance our quality of life. The REU experience provides students with the foundations and inspiration to pursue computing careers and research in areas that are rapidly evolving and are of importance to the nation. The project is led by a team of researchers offering state-of-the-art facilities and professional mentors to guide undergraduates in explorations of problems related to mobile security. Students will learn how to use current tools and techniques to solve those problems that have direct impact on people. The team will use proven strategies to recruit undergraduate students from groups traditionally under-represented in computer science. The students will participate in research and professional development activities all designed to achieve the goals of retaining and graduating undergraduate students in computer science and engineering, and increasing recruitment of students into graduate programs.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.

这个研究经验的本科生（REU）网站奖资金在纽约理工学院的一个优秀的REU网站的更新。该网站将招募来自全国各地的本科生参与与保护智能手机和无线网络等移动的设备相关的研究。保护移动的设备和网络是当前感兴趣的领域，非常适合本科生的研究生产力。学生们将通过创建新的生物识别技术来确保移动的设备和无线网络的安全，为移动的设备上的无缝和安全的用户身份验证开发节能，隐私保护的加密协议，检测恶意软件和识别移动的设备的攻击，以保护数据的完整性和隐私，以及保护医疗设备和数据。这个网站应该有助于培养一批计算专业人员，他们可以设计影响社会和提高我们生活质量的未来系统。REU的经验为学生提供了基础和灵感，以追求计算职业和研究领域正在迅速发展，是重要的国家。该项目由一组研究人员领导，提供最先进的设施和专业导师，指导本科生探索与移动的安全相关的问题。学生将学习如何使用当前的工具和技术来解决那些对人们有直接影响的问题。该团队将使用经过验证的策略，从传统上在计算机科学领域代表性不足的群体中招募本科生。学生将参加研究和专业发展活动，所有这些活动都是为了实现留住和毕业计算机科学和工程专业的本科生，并增加学生进入研究生课程的目标。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Toxic Comment Detection: Analyzing the Combination of Text and Emojis

有毒评论检测：分析文本和表情符号的组合

DOI：
发表时间：
2021
期刊：
The Seventh National Workshop for REU Research in Networking and Systems (REUNS 2021
影响因子：
0
作者：
Aquino, Michael;Ortiz, Yasiris;Rashid, Arif;Tumlin, Anne M.;Artan, N. Sertac;Dong, Ziqian;Gu, Huanying
通讯作者：
Gu, Huanying

Multimodal Emotion Recognition with Surgical and Fabric Masks

使用外科口罩和织物口罩进行多模式情绪识别

DOI：
10.1109/icassp43922.2022.9746414
发表时间：
2022
期刊：
Speech and Signal Processing (ICASSP
影响因子：
0
作者：
Yang, Ziqing;Nayan, Katherine;Fan, Zehao;Cao, Houwei
通讯作者：
Cao, Houwei

Analysis of Eye Fixations During Emotion Recognition in Talking Faces

说话面孔情绪识别过程中眼睛注视的分析

DOI：
10.1109/acii52823.2021.9597440
发表时间：
2021
期刊：
2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII
影响因子：
0
作者：
Cao, Houwei;Elliott, Forest
通讯作者：
Elliott, Forest

EnsembleDroid: A Malware Detection Approach for Android System based on Ensemble Learning