REU Site: Research Experiences for Undergraduates - Emerging Issues in Cybersecurity

REU 网站：本科生研究经验 - 网络安全中的新问题

• 批准号: 1757945
• 负责人: Jun Zheng
• 金额: $ 28.8万
• 依托单位: New Mexico Institute of Mining and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1757945&HistoricalAwards=false
• 关键词: REU; Site; Research; Experiences; Undergraduates

This funding establishes a new Research Experiences for Undergraduates (REU) Site at the New Mexico Institute of Mining and Technology. This REU Site will host a diverse group of undergraduate students who will spend their summer working on research problems dealing with emerging areas of cybersecurity. Sound approaches to cybersecurity are essential for protecting the computer and communication devices of individual consumers as well as public and private companies and organizations and the government from the increasing threats of cyber attacks while also preserving the privacy concerns of citizens. The projects are led by faculty from the Computer Science and Engineering Department who are actively engaged in cybersecurity research. The university faculty will collaborate with RiskSense, a cybersecurity company, to develop research projects that have real-world relevance and that can impact the practices of the cybersecurity industry. The site will provide mentoring and other professional development opportunities that should impact the future academic and professional careers of the students. The site plans to attract talented undergraduate students from universities in New Mexico and the surrounding southwestern area of the country and target students from groups traditionally under-represented in the computing fields. The research projects in this REU Site address a range of topics including document format malware detection, machine learning for botnet detection, access control for smart grid, privacy-preserving location-based services, secure data logging for mobile devices, and developing user mental models against semantic attacks. Students will use state-of-the-art facilities and current methods and techniques to develop solutions to fundamental challenges in cybersecurity. The goals of this site include providing students experience in cutting edge research in cybersecurity, broadening participation in computing, motivating students to pursue graduate studies in computer science, and preparing the future cybersecurity workforce. This site provides an immersive research environment and cutting-edge research projects to undergraduate students who have limited access to research experiences and cybersecurity experts. The site will be one of the first REU Sites in the southwest region of the country focusing on cybersecurity. The site has the potential to impact the cybersecurity workforce of the region. Through participation in the summer research program, the undergraduate students should acquire skills that will lead to rewarding professional careers in a field of emerging importance and impact.

这笔资金在新墨西哥州矿业与技术学院建立了一个新的本科生研究体验(REU)网站。这个REU网站将接待一群不同的本科生，他们将在夏天致力于研究处理新兴网络安全领域的问题。健全的网络安全方法对于保护个人消费者以及公共和私人公司和组织以及政府的计算机和通信设备免受日益增长的网络攻击威胁至关重要，同时也要保护公民的隐私关切。这些项目由计算机科学与工程系的教员领导，他们积极参与网络安全研究。该大学的教职员工将与网络安全公司RiskSense合作，开发与现实世界相关、能够影响网络安全行业实践的研究项目。该网站将提供指导和其他职业发展机会，这些机会应该会对学生未来的学术和职业生涯产生影响。该网站计划吸引来自新墨西哥州和美国西南部周边地区大学的有才华的本科生，目标学生来自传统上在计算机领域代表性不足的群体。该REU网站的研究项目涉及一系列主题，包括文档格式恶意软件检测、僵尸网络检测的机器学习、智能电网的访问控制、隐私保护的基于位置的服务、移动设备的安全数据记录，以及开发针对语义攻击的用户心理模型。学生将使用最先进的设施和当前的方法和技术来开发解决网络安全中的根本挑战的解决方案。该网站的目标包括为学生提供网络安全前沿研究的经验，扩大对计算的参与，激励学生攻读计算机科学研究生课程，并为未来的网络安全工作人员做好准备。该网站为接触有限研究经验和网络安全专家的本科生提供身临其境的研究环境和前沿研究项目。该网站将是该国西南部地区首批关注网络安全的REU网站之一。该网站有可能影响该地区的网络安全劳动力。通过参加暑期研究计划，本科生应该获得一些技能，这些技能将在一个新出现的重要性和影响力的领域里带来职业生涯的回报。

项目成果

Developing a mental model for use in the context of computer security

开发用于计算机安全的心理模型

• DOI: 10.1145/3297280.3299748
• 发表时间: 2019
• 期刊: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
• 作者: Liljestrand, Isaiah;Gonzales, Marcelo;Shin, Dongwan
• 通讯作者: Shin, Dongwan

Robust PDF Malware Detection with Image Visualization and Processing Techniques

利用图像可视化和处理技术进行强大的 PDF 恶意软件检测

• DOI: 10.1109/icdis.2019.00024
• 发表时间: 2019
• 期刊: 2019 2nd International Conference on Data Intelligence and Security (ICDIS
• 作者: Corum, Andrew;Jenkins, Donovan;Zheng, Jun
• 通讯作者: Zheng, Jun

Behavior control-based approach to influencing user's cybersecurity actions using mobile news app

基于行为控制的方法使用移动新闻应用程序影响用户的网络安全行为

• DOI: 10.1145/3412841.3442103
• 发表时间: 2021
• 期刊: The 36th Annual ACM Symposium on Applied Computing
• 作者: Lombardi, Vincent;Ortiz, Sarah;Phifer, Jen;Cerny, Tomas;Shin, Dongwan
• 通讯作者: Shin, Dongwan

Using Polygonal Data Clusters to Investigate LIME

使用多边形数据集群研究 LIME

• DOI: 10.20533/isociety.2021.0010
• 发表时间: 2021
• 期刊: International Conference on Information Society (i-Society 2021
• 作者: He, Jesse;Mazumdar, Subhasish
• 通讯作者: Mazumdar, Subhasish

Detecting Cyber Attacks in Smart Grids Using Semi-Supervised Anomaly Detection and Deep Representation Learning

• DOI: 10.3390/info12080328
• 发表时间: 2021-08
• 期刊: Inf.
• 作者: Ruobin Qi;Craig Rasband;Jun Zheng;Raul Longoria