REU Site: Undergraduate Research Experience for Women in Machine Learning-based Cybersecurity

San Jose State University Foundation, San Jose CA

Investigators

Abstract

This project will establish a Research Experiences for Undergraduates (REU) Site at San Jose State University, a Hispanic-serving institute that provides machine learning-based cybersecurity research experience for women. Cybersecurity is of fundamental importance to the economic strength and national security of the United States in order to form a safe and innovative foundation in cyberspace. This project contributes to the future cybersecurity workforce development while the REU site focuses on diversity and quality of cybersecurity workforce education through training female students in newly emerging cybersecurity topics under close mentorships. This REU program will improve their cybersecurity research skills through well-defined project problem sets based on machine learning (ML) and deep learning (DL)-based cybersecurity techniques. This program will have the following societal impacts. (1) The research in the REU site will train minority students to improve their cybersecurity skills through intensive research training in ML/DL-based cybersecurity techniques. (2) The hands-on lab exercises to understand each research topic will help forge the path for students to pursue careers in cybersecurity. (3) This program can drive the broad participation of women in cybersecurity and reduce the gender gap in cybersecurity fields. The REU research projects include various important research topics in ML/DL-based cybersecurity techniques in the different research areas. In particular, the REU site focuses on training women in ML/DL-based cybersecurity research areas according to the following ultimate goals: (1) engaging female students with the real-world security challenges in emerging cybersecurity topics in malware analysis, network data analysis, and social engineering data analysis, (2) initiating and supporting discussions and collaborations with academic mentors to improve awareness, education, and research in the ML/DL-based cybersecurity problems, (3) enhancing cybersecurity skillsets through well-defined cybersecurity research projects in important topics, and (4) developing their professional career with experts from academia and industry that can cover data science techniques and cybersecurity areas. The research projects aim (1) to train students in new important cybersecurity problems in the new areas through our faculty research outcomes for their skill improvements and (2) to build up research capabilities in cybersecurity fields through hands-on exercises with our existing labs that each faculty mentor has. Related software and research materials will be made available on the project website. 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.