Nate Mathews

Nate Mathews

PhD Candidate specializing in the application of machine learning and deep learning techniques to address cybersecurity challenges, particularly in network traffic analysis.

CSEC-559/659 Generative AI in Cybersecurity

Graduate/Undergraduate course, Rochester Institute of Technology, Department of Computing Security, 2024

In this project-based course, students explore the application of generative AI models, such as large language models (LLMs), to various cybersecurity tasks. Students gain hands-on experience working with AI tools, critiquing their application in real-world scenarios, and understanding the implications of using generative AI in cybersecurity.

Course Learning Outcomes

  • Explain how generative AI models work and evaluate their capabilities and limitations in cybersecurity.
  • Identify and apply generative AI models to tasks like incident response, threat intelligence, and penetration testing.
  • Develop, implement, and report on case studies applying generative AI to real-world cybersecurity problems.
  • Discuss the ethical implications and legal considerations of using generative AI in cybersecurity.

Key Projects

  • Project 1: Apply prompt engineering methods to produce cybersecurity-related documents.
  • Project 2: Use generative AI to assist in program development.
  • Project 3: Fine-tune or apply retrieval-augmented generation (RAG) techniques to create a specific AI model for cybersecurity use.

Our experiences developing and running this course has been published as an article in 28th Colloquium for Information Systems Security Education (CISSE) titled “Teaching Generative AI for Cybersecurity: A Project-Based Learning Approach”.