Date of Graduation

5-2026

Document Type

Thesis

Degree Name

Bachelor of Science in Computer Science

Degree Level

Undergraduate

Department

Computer Science and Computer Engineering

Advisor/Mentor

Alexander Nelson

Committee Member

Qinghua Li

Second Committee Member

John Gauch

Third Committee Member

n/a

Fourth Committee Member

n/a

Fifth Committee Member

n/a

Abstract

In the world of cybersecurity, the rapid development of artificial intelligence proposes a constant challenge for researchers to defend critical infrastructure. Attacks on critical infrastructure can be catastrophic, and emerging strategies of cyber-adversaries that implement leading AI models can expose vulnerabilities in critical infrastructure much faster than previous tools. To defend against this emerging threat, the Cybersecurity Research Working Group at the University of Arkansas is aiming to develop a cross-domain and cross-discipline center of excellence. To support this effort, the group is writing a literature review on the topics of AI and critical systems security. Literature review is an important process that helps researchers identify gaps or trends in knowledge, in order to direct future efforts, but it takes time and effort that could otherwise be used on new research. Large language models (LLMs), which are particularly well-equipped for identifying text relationships, pose a potential solution to this lack of efficiency in literature review. In this thesis, I explore one method of developing literature review across domains in cybersecurity through the use of popular AI chatbots and LLMs like ChatGPT, Google Gemini, and NotebookLM, in the hopes that it will provide a workable basis of a deeper review for the Cybersecurity Research Working Group.

Keywords

Artificial Intelligence, Cybersecurity, Literature Review

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