Daniel (Ozzy) Olszewski

Daniel (Ozzy) Olszewski

I am a PhD candidate at the University of Florida with research interests in computer security, systems, and reproducibility. My work explores practical solutions to complex systems problems through secure design and empirical analysis.

Posts by Collection

portfolio

publications

SMS OTP Security (SOS): Hardening SMS-Based Two Factor Authentication

Published in Proceedings of the ACM Asia Conference on Computer and Communications Security 2022 (ASIA CCS 22), 2022

Proposes improved mechanisms to strengthen SMS-based two-factor authentication against known threats.

Recommended citation: Peeters, C., Patton, C., Sherman, I., Olszewski, D., Shrimpton, T., & Traynor, P. (2022). SMS OTP Security (SOS): Hardening SMS-Based Two Factor Authentication. In Proceedings of ASIA CCS 2022.
Download Paper

HallMonitor: A Framework for Identifying Network Policy Violations in Software

Published in Proceedings of the IEEE Conference on Communication and Network Security 2022 (CNS 22), 2022

Introduces HallMonitor, a system for identifying and analyzing network policy violations in software applications.

Recommended citation: Olszewski, D., Zhu, W., Sathyanarayana, S., Butler, K., & Traynor, P. (2022). HallMonitor: A Framework for Identifying Network Policy Violations in Software. In Proceedings of IEEE CNS 2022.
Download Paper

Efficient Malware Analysis Using Metric Embeddings

Published in Proceedings of the Conference on Applied Machine Learning for Information Security 2022 (CAMLIS 22), 2022

Proposes an efficient malware analysis framework using metric embeddings for scalable classification.

Recommended citation: Rudd, E., Krisiloff, D., Olszewski, D., Raff, E., & Holt, J. (2022). Efficient Malware Analysis Using Metric Embeddings. In Proceedings of CAMLIS 2022.
Download Paper

Get in Researchers; We’re Measuring Reproducibility: A Reproducibility Study of ML Papers at Tier 1 Security Conferences

Published in Proceedings of the ACM Conference on Computer and Communications Security 2023 (ACM CCS '23), 2023

Evaluates reproducibility practices in machine learning papers across top-tier security conferences.

Recommended citation: Olszewski, D., et al. (2023). Get in Researchers; We're Measuring Reproducibility: A Reproducibility Study of ML Papers at Tier 1 Security Conferences. In Proceedings of the ACM CCS 2023.
Download Paper

SoK: The Good, The Bad, and The Unbalanced: Measuring Structural Limitations of Deepfake Media Datasets

Published in Proceedings of the USENIX Security Symposium 2024 (USENIX 24), 2024

Systematizes knowledge on deepfake media datasets and evaluates their structural limitations.

Recommended citation: Layton, S., Tucker, T., Olszewski, D., Warren, K., Butler, K., & Traynor, P. (2024). SoK: The Good, The Bad, and The Unbalanced: Measuring Structural Limitations of Deepfake Media Datasets. In Proceedings of the USENIX Security Symposium 2024 (USENIX 24).
Download Paper

Better Be Computer or I’m Dumb: A Large-Scale Evaluation of Humans as Audio Deepfake Detectors

Published in Proceedings of the ACM Conference on Computer and Communications Security 2024 (ACM CCS '24), 2024

Presents a large-scale human study assessing the ability of people to detect audio deepfakes.

Recommended citation: Warren, K., Tucker, T., Crowder, A., Olszewski, D., Lu, A., Federle, C., Pasternak, M., Layton, S., Butler, K., Gates, C., & Traynor, P. (2024). Better Be Computer or I'm Dumb: A Large-Scale Evaluation of Humans as Audio Deepfake Detectors. In Proceedings of the ACM CCS 2024. Distinguished Paper Award.
Download Paper

I Can Show You the World (of Censorship): Extracting Insights from Censorship Measurement Data Using Statistical Techniques

Published in Proceedings of the Annual Computer Security Applications Conference 2024 (ACSAC 24), 2024

Uses statistical techniques to analyze global censorship measurement data and reveal emerging trends.

Recommended citation: Crowder, A., Olszewski, D., Traynor, P., & Butler, K. (2024). I Can Show You the World (of Censorship): Extracting Insights from Censorship Measurement Data Using Statistical Techniques. In Proceedings of ACSAC 2024.
Download Paper

Characterizing the Impact of Audio Deepfakes in the Presence of Cochlear Implants

Published in Proceedings of the Network and Distributed System Security Symposium 2025 (NDSS 25), 2025

Studies how audio deepfakes affect individuals with cochlear implants, exploring perceptual and security challenges.

Recommended citation: Pasternak, M., Warren, K., Olszewski, D., Traynor, P., & Butler, K. (2025). Characterizing the Impact of Audio Deepfakes in the Presence of Cochlear Implants. In Proceedings of the Network and Distributed System Security Symposium 2025 (NDSS 25).
Download Paper

Reproducibility in Applied Security Conferences: An 11-Year Review on Artifacts and Evaluation Committees

Published in Proceedings of the ACM Conference on Reproducibility 2025 (ACM REP 25), 2025

An 11-year analysis of reproducibility efforts, artifact practices, and evaluation committees across applied security conferences.

Recommended citation: Olszewski, D., Lu, A., Crowder, A., Bennett, N., Layton, S., Bhupathiraju, S.H.V., Tucker, T., Kalgutkar, S., Ver Helst, H., Stillman, C., Butler, K., Rampazzi, S., & Traynor, P. (2025). Reproducibility in Applied Security Conferences: An 11-Year Review on Artifacts and Evaluation Committees. In Proceedings of the ACM Conference on Reproducibility 2025 (ACM REP 25). Best Paper Award.
Download Paper

Towards a Unified Approach of Applied Replicability for Computer Security

Published in Proceedings of the USENIX Security Conference 2025 (USENIX 25), 2025

Proposes a framework for unifying replicability practices in applied computer security research.

Recommended citation: Olszewski, D., Tucker, T., Butler, K., & Traynor, P. (2025). Towards a Unified Approach of Applied Replicability for Computer Security. In Proceedings of the USENIX Security Conference 2025 (USENIX 25).
Download Paper

Analyzing the AI Nudification Application Ecosystem

Published in Proceedings of the USENIX Security Conference 2025 (USENIX '25), 2025

Analyzes the ecosystem of AI nudification applications, their privacy implications, and societal risks. Distinguished Paper Award. Internet Defense Runner-Up Award.

Recommended citation: Gibson, C., Olszewski, D., Brigham, N., Crowder, A., Butler, K., Traynor, P., Redmiles, E., & Kohno, T. (2025). Analyzing the AI Nudification Application Ecosystem. In Proceedings of the USENIX Security Conference 2025 (USENIX '25).
Download Paper

Every breath you don’t take: Deepfake speech detection using breath

Published in Digital Threats: Research and Practice, 2025

This work proposes using breath detection as a high-level feature to distinguish real from deepfake speech, demonstrating its effectiveness on a custom, publicly available dataset of news audio.

Recommended citation: Layton, S., De Andrade, T., Olszewski, D., Warren, K., Gates, C., Butler, K., & Traynor, P. (2025). Every breath you don't take: Deepfake speech detection using breath. Digital Threats: Research and Practice, 6(3), 1-18.
Download Paper

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.