Collaborative Research: EAGER SaTC-EDU: Artificial Intelligence and Cybersecurity: From Research to the Classroom

One of the most critical security challenges of the 21st century is protecting the cyber-physical systems that manage and control our infrastructure, vehicles, homes, and personal devices as well as the information that they store, use and exchange. Artificial intelligence (AI) and machine learning-based tools can help human analysts sort through large volumes of data to determine if an attack on these systems has happened.

Yet, AI components are also vulnerable to attacks, and require development of techniques to make them more robust. This collaborative project between the University of Maryland Baltimore County (UMBC) and the University of Illinois addresses the research and educational aspects of combining AI and cybersecurity. Educational and training materials will be developed for use by college and university instructors and students and by cybersecurity and AI professionals.

These materials will address how AI can improve security systems and how cybersecurity analytics can protect AI systems. In addition, the project will recruit students from groups that have been traditionally underrepresented in computing.

This project has three interrelated topics. The first focuses on education and extends the project team’s existing cybersecurity concept inventory to include relevant AI-related concepts. Student knowledge and understanding of cybersecurity and AI relatedness will be assessed before and after taking AI or cybersecurity courses.

Educational materials and projects will also be created to demonstrate how AI can be applied to cybersecurity problems and how cybersecurity tools can protect AI systems from attack. The second topic explores how the latest AI tools can support cybersecurity tasks. The creation and maintenance of semantic knowledge graphs of cyberthreat information will be studied and used to support reinforcement learning systems that are better at detecting the presence of malware in a host.

The third topic focuses on finding new ways that cybersecurity tools can protect AI systems from becoming compromised by attacks such as data poisoning. Cyberthreat knowledge graphs and neural networks will be used to detect and eliminate likely disinformation from data used to train AI-based cybersecurity systems. This aspect of the project has applications beyond cybersecurity, such as countering disinformation.

This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case specifically cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.

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.