EXPLORATION OF AI-ENABLED CONTENTS FOR UNDERGRADUATE CYBERSECURITY PROGRAMS
Principal Investigator: Dr. Ramoni Lasisi, CIS
Cadets Researchers: Matthew Menia, Zachary Farr, and Corey Jones
The increasing cyber threats, security breaches, and cyber-attacks on key network infrastructure today account for one of many organizations huge amount of loss in terms of money, denial of services, privacy control compromise, and reputation costs . These threats are at scale as sophisticated cyber crimes are now been committed using artificial intelligence (AI) technologies. The increase in sophistication of cyber attacks by many organizations has also recently necessitates the employment of AI as key tool to counter cyber-attacks . There is thus the need to train cyber analysts and other security professionals to learn, understand, and be able to use AI-enabled cybersecurity technologies to address the continuity of cyber-attacks. However, of the 24 undergraduate cybersecurity programs  that we surveyed to understand their relevance to the state of the art and ensure that students are being prepared to be AI-enabled cybersecurity professionals or experts, only one of them has in its curriculum a course in AI or AI-related topics. Furthermore, at this only institution, the AI course focuses on exploring the technologies used to construct computer based agents that perceive, represent knowledge, search spaces, and learn." No mention of cybersecurity or its applications in cybersecurity is found in the course description. This research work investigates the AI courses and/or AI-related topics that are needed to enhance cybersecurity education at the undergraduate level and provide appropriate recommendation towards preparation of the future AI-enabled cybersecurity leaders and experts. Our working hypothesis is stated below:
The inclusion of AI courses or topics in the curricula of undergraduate cybersecurity programs, either by way of dedicated courses or AI modules in multiple classes will help students to learn how to use, develop, and integrate AI technologies in combating cyber attacks, and thus preparing them to be AI-enabled cybersecurity professionals or experts that are workforce-ready.
INDUSTRY-UNIVERSITY INTERNSHIP SYSTEM FOR CYBERSECURITY WORKFORCE
Principal Investigator: Youna Jung, CIS
Co-PI: LTC Jennifer E. Gerow,
This project aims to create an industry-university internship program by leveraging a CIS-specific cybersecurity internship course with a quality improvement system. This program will help CIS cadets fully utilize their internship experiences in cybersecurity companies /centers by connecting their academic background in computer science topics to their internship experiences.
Towards this goal, this program will achieve the goals described above by using the following approaches:
1) Development of CIS Internship Course
- Customize the ECBU department’s internship course to CIS cadets
- An instructor of the course will teach computer science knowledge and skills related to cadets’ internship experiences.
2) Development of Quality Improvement System for Internship Experiences on Cybersecurity
- Development of evaluation system for internship performance to measure cadets’ performance on their internship
- Development of a quality improvement system
PROPOSAL FOR REVERSE ENGINEERING FOR THE INTERNET OF THINGS
Principal Investigator: Dr. Mohamed Azab, CIS
MATHEMATICS FOR CYBERSECURITY EDUCATION
Principal Investigator: COL Dimplekumar Chalishajar, CIS
Principal Investigator: Dr. Abibat Lasisi, CIS
Cybersecurity is a technical field that requires strong quantitative skills. Cybersecurity as a science requires logical problem-solving skills, critical and creative thinking, and decision-making skills which can be learned from exposure to mathematics courses. The field of cybersecurity needs the students to be fully prepared with the knowledge and understanding of some basic mathematics that will aid and improve the study of the subject contents. This research project seeks to explore the mathematics needed for undergraduate programs in cybersecurity. We are exploring the curriculum of some of the universities that currently offer cybersecurity either as a major or minor in their programs and making the changes as per the requirement to our program.
We will be investigating the mathematics needed for a bachelor’s degrees in Cybersecurity, Computer Science with Concentration in Cybersecurity, and Business and Management with Concentration in Cybersecurity. We will be designing and developing two to three mathematics courses that will combine most, or all of the mathematics needed for undergraduate programs in cybersecurity. The provision and creation of these courses will help to improve students’ logical reasoning, creative, and critical thinking skills. This will in turn help them to make good decision in this decision-driven field. At the same time, this will again provide the instructors teaching the cybersecurity classes with some confidence that the students have the prequisite for the higher-level contents to be delivered.
XR-CEIL: EXTENDED REALITY FOR CYBERSECURITY EXPERIENTIAL AND IMMERSIVE LEARNING
Principal Investigator: Dr. Denis Gracanin, Virginia Tech
Principal Investigator: Col. Mohamed Eltoweissy, CIS
There is a dual relationship between Cybersecurity and XR. Like with any other technology, there is an urgent need to develop and promote a fundamental understanding the impact of accessibility, ethics, inclusion, safety and trust on privacy and security in XR environments. On the other hand, using XR for cybersecurity visualization would allow us to visualize and analyze threats in a profoundly different way, thereby increasing cybersecurity training, education, and analytics capabilities. XR-CEIL: Extended Reality for Cybersecurity Experiential and Immersive Learning is an innovative technique for embodied data exploration and immersive analysis of multi-dimensional heterogeneous data using coordinated multiple views (CMV) in an XR environment. We leverage the physical space, to strategically position multiple coordinated 3D and 2D views in the space, providing affordances for embodied user interaction and linking between the displayed visualizations. Using spatial CMV facilitates embodied depiction of various attributes of multi-dimensional data, especially spatio-temporal data sets.