Mississippi Valley State University’s Mathematics, Computer, and Information Science (MCIS) Department is one of seven Historically Black Colleges and Universities (HBCUs) and three National Research Laboratories which include, Lawrence-Livermore (LLNL), Brookhaven (BNL), and National Renewable Energy (NREL) to develop the AI-CyS research partnership at the intersection of artificial intelligence and cybersecurity.
This project brings together researchers from MVSU, Hampton University, Florida A&M University, Winston-Salem State University, University of District of Columbia, Norfolk State University, and Howard University).
This collaboration is intended to further research and increase research capacity by increasing student and faculty involvement and training. To develop that potential, the project team will leverage its existing research activities and collaborations to deepen relationships with other HBCUs and with the NRLs.
The NRLs will provide additional research resources and mentoring through both regular remote meetings and the provision of opportunities for on-site visits to the NRLs around projects of mutual interest through student internships and faculty visits to the NRLs.
Dr. Latonya Garner-Jackson, chair of the MCIS department, said she is extremely excited about this project. and the opportunity for MVSU students.
“This project creates a great opportunity for our students. The funding, coupled with visiting and partnering with three national laboratories, will assist faculty and students in fortifying their research capabilities in artificial intelligence and cybersecurity,” said Dr. Garner.
The partnership is organized around five seed research projects chosen to maximize existing capacity and collaborations.
The first uses reinforcement learning to improve target selection by current autonomous network mapping software agents. The second involves analyzing network traceroute data to better understand and work around limitations of its ability to map paths to better detect and classify network anomalies. The third focuses on developing, then defending against, adversarial attacks on computer vision algorithms in which attackers add visual patches to objects to fool object detection and classification tools. The fourth analyzes existing tools and algorithms for generating “deep fake” videos to develop methods to detect forged video in near real-time, with applications to surveillance and authentication tasks. The fifth will extend probabilistic sequential models to develop threat detectors at multiple levels of the network stack in the context of the Internet of Things devices.
Cybersecurity vulnerabilities are growing at a scale and speed that strains human capacity to proactively address threats; developing AI and machine learning (ML) techniques to support prediction and detection capabilities is thus an important area of research.
The HBCUs involved in AI-CyS either already have or are developing cybersecurity research capacity. This collaboration is intended to further research and increase research capacity by increasing student and faculty involvement and training.