Thomas G. Podnar
CERT
Publications by Thomas G. Podnar
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ML-Driven Decision-Making in Realistic Cyber Exercises
October 20, 2022 • Podcast
Dustin D. UpdykeThomas G. Podnar
Thomas Podnar and Dustin Updyke discuss efforts by the SEI CERT Division to apply machine learning to increase the realism of non-player characters (NPCs) in cyber training exercises.
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Traditional and Advanced Techniques for Network Beacon Detection
February 01, 2022 • Video
Dustin D. UpdykeThomas G. Podnar
Dustin Updyke and Tom Podnar delivered this presentation at FloCon 2022 on January 12, 2022. Watch the video and download the slides.
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Using Machine Learning to Increase NPC Fidelity
December 01, 2021 • Technical Report
Dustin D. UpdykeThomas G. PodnarGeoffrey B. Dobson
The authors describe how they used machine learning (ML) modeling to create decision-making preferences for non-player characters (NPCs).
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Foundation of Cyber Ranges
May 19, 2021 • Technical Report
Thomas G. PodnarGeoffrey B. DobsonDustin D. Updyke
This report details the design considerations and execution plan for building high-fidelity, realistic virtual cyber ranges that deliver maximum training and exercise value for cyberwarfare participants.
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GHOSTS in the Machine: A Framework for Cyber-Warfare Exercise NPC Simulation
December 03, 2018 • Technical Report
Dustin D. UpdykeGeoffrey B. DobsonThomas G. Podnar
This report outlines how the GHOSTS (General HOSTS) framework helps create realism in cyber-warfare simulations and discusses how it was used in a case study.
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R-EACTR: A Framework for Designing Realistic Cyber Warfare Exercises
September 29, 2017 • Technical Report
Geoffrey B. DobsonThomas G. PodnarAdam D. Cerini
R-EACTR is a design framework for cyber warfare exercises. It ensures that designs of team-based exercises factor realism into all aspects of the participant experience.
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