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.