PenQuestEnv: A Reinforcement Learning Environment for Cyber Security

Sebastian Eresheim, Sebastian Eresheim, Simon Gmeiner, Alexander Piglmann, Thomas Petelin, Robert Luh, Paul Tavolato, Sebastian Schrittwieser, Sebastian Schrittwieser

2025

Abstract

We present PenQuestEnv, a reinforcement learning environment for the digital board game PenQuest. PenQuest is a cyber security strategic attack and defense simulation game that enables players to carry out cyber attacks and defenses in specific scenarios, without the need for technical know-how. Its two-player setup is highly customizable and allows to model a versatile set of scenarios in which players need to find optimal strategies to achieve their goals. This environment enables the training of reinforcement learning agents for finding optimal attack and defense strategies in a variety of different scenarios and multiple different game options. With this work we intend to ignite future research on multipurpose cyber security strategies, where a single agent is capable of finding optimal strategies against a versatile set of opponents in different scenarios.

Paper Citation

in Harvard Style

Eresheim S., Gmeiner S., Piglmann A., Petelin T., Luh R., Tavolato P. and Schrittwieser S. (2025). PenQuestEnv: A Reinforcement Learning Environment for Cyber Security. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 217-224. DOI: 10.5220/0013122700003899

in Bibtex Style

@conference{icissp25,
author={Sebastian Eresheim and Simon Gmeiner and Alexander Piglmann and Thomas Petelin and Robert Luh and Paul Tavolato and Sebastian Schrittwieser},
title={PenQuestEnv: A Reinforcement Learning Environment for Cyber Security},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2025},
pages={217-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013122700003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - PenQuestEnv: A Reinforcement Learning Environment for Cyber Security
SN - 978-989-758-735-1
AU - Eresheim S.
AU - Gmeiner S.
AU - Piglmann A.
AU - Petelin T.
AU - Luh R.
AU - Tavolato P.
AU - Schrittwieser S.
PY - 2025
SP - 217
EP - 224
DO - 10.5220/0013122700003899
PB - SciTePress