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Authors: Richard Elderman 1 ; Leon J. J. Pater 1 ; Albert S. Thie 1 ; Madalina M. Drugan 2 and Marco M. Wiering 1

Affiliations: 1 University of Groningen, Netherlands ; 2 Technical University of Eindhoven, Netherlands

Keyword(s): Reinforcement Learning, Adversarial Setting, Markov Games, Cyber Security in Networks.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Multi-Agent Systems ; Privacy, Safety and Security ; Soft Computing ; Software Engineering ; Symbolic Systems

Abstract: This paper focuses on cyber-security simulations in networks modeled as a Markov game with incomplete information and stochastic elements. The resulting game is an adversarial sequential decision making problem played with two agents, the attacker and defender. The two agents pit one reinforcement learning technique, like neural networks, Monte Carlo learning and Q-learning, against each other and examine their effectiveness against learning opponents. The results showed that Monte Carlo learning with the Softmax exploration strategy is most effective in performing the defender role and also for learning attacking strategies.

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Paper citation in several formats:
Elderman, R.; J. J. Pater, L.; S. Thie, A.; M. Drugan, M. and M. Wiering, M. (2017). Adversarial Reinforcement Learning in a Cyber Security Simulation. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-220-2; ISSN 2184-433X, SciTePress, pages 559-566. DOI: 10.5220/0006197105590566

@conference{icaart17,
author={Richard Elderman. and Leon {J. J. Pater}. and Albert {S. Thie}. and Madalina {M. Drugan}. and Marco {M. Wiering}.},
title={Adversarial Reinforcement Learning in a Cyber Security Simulation},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2017},
pages={559-566},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006197105590566},
isbn={978-989-758-220-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Adversarial Reinforcement Learning in a Cyber Security Simulation
SN - 978-989-758-220-2
IS - 2184-433X
AU - Elderman, R.
AU - J. J. Pater, L.
AU - S. Thie, A.
AU - M. Drugan, M.
AU - M. Wiering, M.
PY - 2017
SP - 559
EP - 566
DO - 10.5220/0006197105590566
PB - SciTePress