REU Site: Research Experience for Undergraduates in Data-Centric Computing

REU 网站：以数据为中心的计算本科生的研究经验

• 批准号: 1659755
• 负责人: Shou-Hsuan Huang
• 金额: $ 36万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659755&HistoricalAwards=false
• 关键词: REU; Site; Research; Experience; Undergraduates

The Department of Computer Science at the University of Houston proposes a Research Experience for Undergraduates (REU) site program in the area of Data-Centric Computing. This is a three-year program in which each year a group of ten students is selected nationally, with emphasis on underrepresented cohorts in Computer Science. In order to provide greater focus, the topics are all in the area of Data-Centric Computing. We have proposed a variety of projects arranged around this theme. Topics include Cybersecurity (Phishing Detection, Network Intrusion Detection, and Privacy), Text and Video Mining, and Bioinformatics. The goal of this REU program is to expose undergraduate students, through carefully selected topics, to data science research methodologies. The participants will acquire experience in conducting research through their involvement in the research on their individually assigned topics, under the guidance of a faculty mentor. This exposes young researchers to core principles of research, namely objectivity and verifiability. Participants will also be introduced to specific research methods, literature review, and ethics in science and technology, and acquire writing and presentation skills. Additionally, participants will learn about career opportunities, the graduate application process, and survivals skills in graduate programs. In this way, the program serves the national interest, by contributing to the progress of science, through the results obtained by the undergraduate students in this program and in the future, when the students enroll in graduate programs and conduct further research.

休斯顿大学计算机科学系提出了一个以数据为中心的计算领域的本科生研究经验（REU）网站计划。这是一个为期三年的计划，每年在全国范围内选出一组10名学生，重点是计算机科学中代表性不足的群体。为了提供更大的关注，主题都在以数据为中心的计算领域。我们围绕这个主题提出了各种项目。主题包括网络安全（网络钓鱼检测，网络入侵检测和隐私），文本和视频挖掘以及生物信息学。 这个REU计划的目标是通过精心挑选的主题，让本科生接触数据科学研究方法。参与者将通过参与他们各自分配的主题的研究，在教师导师的指导下获得进行研究的经验。这使年轻的研究人员接触到研究的核心原则，即客观性和可核查性。参与者还将被介绍到具体的研究方法，文献综述和科学技术伦理，并获得写作和演讲技巧。此外，参与者将了解研究生课程中的职业机会、研究生申请流程和生存技能。通过这种方式，该计划服务于国家利益，通过促进科学的进步，通过本科生在该计划中获得的结果，并在未来，当学生参加研究生课程并进行进一步的研究。

项目成果

Data Quality for Security Challenges: Case Studies of Phishing, Malware and Intrusion Detection Datasets

• DOI: 10.1145/3319535.3363267
• 发表时间: 2019-11
• 期刊: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Rakesh M. Verma;Victor Zeng;Houtan Faridi

Phishing Email Detection Using Robust NLP Techniques

使用强大的 NLP 技术检测网络钓鱼电子邮件

• DOI: 10.1109/icdmw.2018.00009
• 发表时间: 2018
• 期刊: IEEE International Conference on Data Mining Workshops
• 作者: Egozi, Gal;Verma, Rakesh
• 通讯作者: Verma, Rakesh

Detecting Intruders by User File Access Patterns

通过用户文件访问模式检测入侵者

• DOI: 10.1007/978-3-030-36938-5_19
• 发表时间: 2019
• 期刊: International Conference on Network and System Security
• 作者: Shou-Hsuan S. Huang, Zechun Cao

PhishBench 2.0: A Versatile and Extendable Benchmarking Framework for Phishing.

PhishBench 2.0：多功能且可扩展的网络钓鱼基准测试框架。

• DOI: 10.1145/3372297.3420017
• 发表时间: 2020
• 期刊: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
• 作者: Zeng, V.

Diverse Datasets and a Customizable Benchmarking Framework for Phishing

• DOI: 10.1145/3375708.3380313
• 发表时间: 2020-03
• 期刊: Proceedings of the Sixth International Workshop on Security and Privacy Analytics
• 作者: Victor Zeng;Shahryar Baki;Ayman El Aassal;Rakesh M. Verma;Luis F. T. Moraes;Avisha Das