2025 ASEE Annual Conference & Exposition

The Weaving of Machine Learning and Artificial Intelligence into the Fabric of Cybersecurity Curriculum: From Degree Plan to Capstone Projects

Presented at Computers in Education Division (COED) Track 2.C

Abstract
As our newly designed degree in Cybersecurity enters its fourth year, students in the program are starting to take courses beyond the basic ones, including senior courses, technical electives, and capstone projects. While Cybersecurity is at the heart of our degree that addresses the national need for cybersecurity specialists, how we approach the education and pedagogy of cybersecurity in the era of Big Data and AI/ML (Artificial Intelligence/Machine Learning) is a question that we are addressing in real-time as techniques and measures and countermeasures of cybersecurity attacks keep evolving and taking advantages of the rapid advancements in computing/server, networking, server, and virtualization technologies.

Educational modules with hands-on labs are available at different junctions to give students in the program multiple chances to incorporate the latest techniques in AI/ML into their degree. Whether it is advising and orientation sessions, seminars and workshops, technical electives, or capstone projects, custom-tailored material has been created specifically for our cybersecurity students. This paper presents our systematic efforts to pervade the curriculum with a hands-on, immersive approach to integrating AI/ML. We will present the following modules that were developed or are currently being developed.

• Early Workshops/Seminars/Sessions
o Advising.
o Degree Plans.
o Departmental.
o Student Clubs.
• The AI/ML teachings in a cybersecurity-focused technical elective (Course was named: CYBI-4336: Cybersecurity Engineering with ML/AI):
o Description of each module.
o The list of objectives and sub-objectives for each module.
o Case studies in Cybersecurity.
• Research opportunities for cybersecurity students in ML/AI:
o Capstone Projects.
o AI/ML public cloud platforms.
o Public tools for collaboration.
• The list of AI/ML hands-on labs using public resources:
o Taxonomy of each technique in the lab.
o Delivery method of each lab.
o Design of lab by instructor.
By presenting our efforts, we hope to benefit from other efforts and that other instructors facing the same issues can benefit from our experience by adopting best practices while avoiding pitfalls.
Keywords: Machine Learning, Artificial Intelligence, Cyber Security, Pair Teaching, Integrative Labs, Project-based Learning.

Authors

Dr. Mahmoud K Quweider The University of Texas Rio Grande Valley [biography]

M K Quweider is a Computer and Cybersecurity Sciences Professor at the U. of Texas at UTRGV. He received his Ph.D. in Engineering Science (Multimedia and Imaging Specialty) and B.S. In Electrical Engineering, M.S. in Applied Mathematics, M.S. in Engineering Science, and M.S. in Biomedical Engineering, all from the University of Toledo, Ohio. He also holds a Bachelor/Master of English and a Master of Business Administration from the University of Texas at Brownsville. He recently obtained a master’s in digital forensics from Champlain College, after which he founded the B.Sc. in Cyber Security. After graduation, he was employed at several corporations, including Pixera, a digital multimedia processing company in Cupertino, CA; 3COM, a networking and communication company in Schaumberg, IL; and Mercantec, an E-Commerce company in Naperville, IL. He has more than 50 publications in the field and has served as a reviewer and moderator for several scientific and educational journals and conferences. He joined UTB (UTRGV) in the Spring of 2000. His areas of interest include AI/Machine Learning, Networking and Cyber Security, and Digital Imaging.
Dr. Liyu Zhang University of Texas Rio Grande Valley
Dr. Jorge Castillo The University of Texas Rio Grande Valley [biography]

Jorge Castillo, Ph.D., is an Assistant Professor at the University of Texas Rio Grande Valley, specializing in Cybersecurity and Electrical Engineering. He earned his Ph.D. in Electrical Engineering from the University of Texas at San Antonio, with a focus on blockchain technology for secure applications. His research interests include blockchain systems, machine learning and privacy-preserving methodologies.
Dr. Ala Qubbaj The University of Texas Rio Grande Valley [biography]

Dr. Ala Qubbaj is the Dean for the College of Engineering and Computer Science at the University of Texas Rio Grande Valley (UTRGV)

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