Nate Mathews - Curriculum Vitae

Education

• Ph.D. in Computing & Information Sciences, Rochester Institute of Technology, 2019 – Present (Expected August 2025)
  • Advised by Dr. Matthew Wright
  • Focus: Deep Learning and Cybersecurity, specializing in website fingerprinting, traffic correlation, and privacy-preserving communication networks.
• B.Sc. in Computing Security, Rochester Institute of Technology, 2015 – 2019
  • Minor in Computer Science

Work Experience

• Academic Research Assistant
  • Rochester Institute of Technology, May 2018 – Present
  • Conducting research on advanced traffic analysis, website fingerprinting, and deep learning in cybersecurity, with a focus on defending privacy in Tor networks and similar anonymity systems.
  • Developed state-of-the-art techniques for both offensive and defensive applications in traffic correlation attacks, leveraging neural networks, GANs, and transformers.
• Course Instructor
  • Rochester Institute of Technology, August 2022 – Present
  • Instructor for “CSEC-520/620: Cyber Analytics & Machine Learning” and co-instructor for “CSEC-559/659: Generative AI in Cybersecurity.” Led undergraduate and graduate students through hands-on, project-based learning on applying machine learning to real-world cybersecurity issues.

Skills

• Machine Learning: Expertise in deep learning frameworks (PyTorch, TensorFlow) and their application to network traffic analysis.
  • Triplet learning, transformers, adversarial networks, metric learning, flow correlation.
• Cybersecurity: In-depth knowledge of traffic correlation attacks, website fingerprinting, and intrusion detection techniques.
  • Focus on privacy-preserving communications (Tor) and advanced traffic defenses.
• Programming: Python, C, Java, Bash, with extensive experience in developing security tools and machine learning models.
• Collaborative Research: Experience leading and mentoring research teams with strong collaborative work across institutions.
• Project Management: Successfully manage multi-faceted research projects, from initial experimentation to publication.

Publications

Teaching Generative AI for Cybersecurity: A Project-Based Learning Approach

Nate Mathews, Christopher Schwartz, Matthew Wright. (2025). “Teaching Generative AI for Cybersecurity: A Project‑Based Learning Approach.” Journal of The Colloquium for Information Systems Security Education, 12(1), 1–10. DOI: 10.53735/cisse.v12i1.211.

LASERBEAK: Evolving Website Fingerprinting Attacks with Attention and Multi-Channel Feature Representation

Nate Mathews, James K. Holland, Nicholas Hopper, Matthew Wright. (2024). "LASERBEAK: Evolving Website Fingerprinting Attacks with Attention and Multi-Channel Feature Representation." IEEE Transactions on Information Forensics and Security (TIFS), 2024. DOI: 10.1109/TIFS.2024.3468171.

WhisperVoiceTrace: A Comprehensive Analysis of Voice Command Fingerprinting

Minji Jo, Hyojin Kim, Jiwoo Hong, Hosung Kang, Nate Mathews, Se Eun Oh. (2024). "WhisperVoiceTrace: A Comprehensive Analysis of Voice Command Fingerprinting." ACM Asia Conference on Computer and Communications Security (ASIA CCS), 2024. DOI: 10.1145/3634737.3657017.

SoK: A Critical Evaluation of Efficient Website Fingerprinting Defenses

Nate Mathews, James K. Holland, Se Eun Oh, Mohammad Saidur Rahman, Nicholas Hopper, Matthew Wright. (2023). "SoK: A Critical Evaluation of Efficient Website Fingerprinting Defenses." IEEE Symposium on Security and Privacy (SP), 2023. DOI: 10.1109/SP46215.2023.10179289.

DeepCoFFEA: Improved Flow Correlation Attacks on Tor via Metric Learning and Amplification

Se Eun Oh, Taiji Yang, Nate Mathews, James K. Holland, Mohammad Saidur Rahman, Nicholas Hopper, Matthew Wright. (2022). "DeepCoFFEA: Improved Flow Correlation Attacks on Tor via Metric Learning and Amplification." IEEE Symposium on Security and Privacy (SP), 2022. DOI: 10.1109/SP46214.2022.9833801.

What a SHAME: Smart Assistant Voice Command Fingerprinting Utilizing Deep Learning

Jack Hyland, Conrad Schneggenburger, Nick Lim, Jake Ruud, Nate Mathews, Matthew Wright. (2021). "What a SHAME: Smart Assistant Voice Command Fingerprinting Utilizing Deep Learning." 20th Workshop on Privacy in the Electronic Society (WPES), 2021. DOI: 10.1145/3463676.3485615.

GANDaLF: GAN for Data-Limited Fingerprinting

Se Eun Oh, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright, Nicholas Hopper. (2021). "GANDaLF: GAN for Data-Limited Fingerprinting." Proceedings on Privacy Enhancing Technologies (PoPETS), 2021. DOI: 10.2478/popets-2021-0029.

Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks With Adversarial Traces

Mohammad Saidur Rahman, Mohsen Imani, Nate Mathews, Matthew Wright. (2020). "Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks With Adversarial Traces." IEEE Transactions on Information Forensics and Security (TIFS), 2020. DOI: 10.1109/TIFS.2020.3039691.

Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks

Mohammad Saidur Rahman, Payap Sirinam, Nate Mathews, Kantha Girish Gangadhara, Matthew Wright. (2020). "Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks." Proceedings on Privacy Enhancing Technologies (PoPETS), 2020. DOI: 10.2478/popets-2020-0043.

Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning

Payap Sirinam, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright. (2019). "Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning." ACM SIGSAC Conference on Computer and Communications Security (CCS), 2019. DOI: 10.1145/3319535.3354217.

Understanding Feature Discovery in Website Fingerprinting Attacks

Nate Mathews, Payap Sirinam, Matthew Wright. (2018). "Understanding Feature Discovery in Website Fingerprinting Attacks." IEEE Western New York Image and Signal Processing Workshop (WNYISPW), 2018. DOI: 10.1109/WNYIPW.2018.8576379.

Talks

Teaching Generative AI for Cybersecurity: A Project-Based Learning Approach

November 27, 2024

Talk at 28th Colloquium for Information Systems Security Education (CISSE), Tampa, Florida

Colloquium Talk: Harnessing AI for Advanced Network Security

October 11, 2024

Talk at Washington University in St. Louis (WashU), St. Louis, Missouri

Harnessing AI for Advanced Network Security: From Attacks on Privacy to Defensive Innovations

October 09, 2024

Talk at Rochester Security Summit (RSS), Rochester, New York

Critical Evaluation of Efficient Website Fingerprinting Defenses

May 01, 2023

Talk at IEEE Symposium on Security and Privacy (S&P), San Francisco, California

GANDaLF: GAN for Data-Limited Fingerprinting

July 12, 2021

Talk at Privacy Enhancing Technologies Symposium (PETS), Virtual Event

Deanonymizing Internet Traffic with Website Fingerprinting

July 27, 2020

Talk at IETF-108, PEARG, Virtual Event

Triplet Fingerprinting: N-shot Learning for Website Fingerprinting

November 11, 2019

Talk at ACM Conference on Computer and Communications Security (CCS), London, UK

Teaching

CSEC-759 Graduate Seminar in Computing Security

CSEC-559/659 Generative AI in Cybersecurity

CSEC 520/620 Cyber Analytics & Machine Learning

Service and Leadership

• Mentor: Lead and mentor undergraduate and graduate students in cybersecurity research, helping them develop machine learning models and perform network traffic analysis and related research.
• Community Impact: Active participant in the NSF CyberAI Working Group, contributing to the development of AI-focused cybersecurity Knowledge Units and curriculum. Reviewer for academic security publications such as T-IFS, PETS, TDSC, ESWA, and COSE.
• Collaborator: Actively collaborating with researchers from the University of Minnesota and other institutions on Tor traffic analysis and adversarial defenses.
• Professional Development: Regular speaker at conferences like IEEE S&P, PETS, and ACM CCS, showcasing cutting-edge research in traffic analysis and machine learning in security.