Adversarial Reinforcement Learning in a Cyber Security Simulation

Richard Elderman, Leon J. J. Pater, Albert S. Thie, Madalina M. Drugan, Marco M. Wiering

2017

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.

Paper Citation

in Harvard Style

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 2: ICAART, ISBN 978-989-758-220-2, pages 559-566. DOI: 10.5220/0006197105590566

in Bibtex Style

@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 2: ICAART,},
year={2017},
pages={559-566},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006197105590566},
isbn={978-989-758-220-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Adversarial Reinforcement Learning in a Cyber Security Simulation
SN - 978-989-758-220-2
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