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.

Teaching

CSEC-759 Graduate Seminar in Computing Security

Graduate course, Rochester Institute of Technology, Department of Computing Security, 2025

This research-oriented seminar explores the latest advancements in malware analysis, focusing on techniques to detect, analyze, and counteract malicious software. Students engage with state-of-the-art research, apply malware analysis tools, and examine strategies used by attackers to evade detection. The course emphasizes hands-on experience with tools such as Cuckoo Sandbox, Volatility, and Google Rapid Response (GRR) while fostering critical analysis of recent research papers.

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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.

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CSEC 520/620 Cyber Analytics & Machine Learning

Graduate/Undergraduate course, Rochester Institute of Technology, Department of Computing Security, 2022

This course provides students with the opportunity to explore methods and applications in cyber analytics using advanced machine learning algorithms, including deep learning. The course covers both foundational machine learning techniques and their applications to real-world cybersecurity problems, such as network anomaly detection, malware analysis, and intrusion detection.

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