Improving Intrusion Detection Systems with Multi-Agent Deep Reinforcement Learning: Enhanced Centralized and Decentralized Approaches

Amani Bacha, Farah Barika Ktata, Faten Louati

2023

Abstract

Intrusion detection is a crucial task in the field of computer security as it helps protect these systems against malicious attacks. New techniques have been developed to cope with the increasing complexity of computer systems and the constantly evolving threats. Multi-agent reinforcement learning (MARL), is an extension of Reinforcement Learning (RL) in which agents can learn to detect and respond to intrusions while considering the actions and decisions of the other agents. In this study, we evaluate MARL’s performance in detecting network intrusions using the NSL-KDD dataset. We propose two approaches, centralized and decentralized, namely COCA-MADQN and MADQN-GTN. Our approaches show good results in terms of Accuracy, Precision, Recall, and F1-score.

Paper Citation

in Harvard Style

Bacha A., Barika Ktata F. and Louati F. (2023). Improving Intrusion Detection Systems with Multi-Agent Deep Reinforcement Learning: Enhanced Centralized and Decentralized Approaches. In Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-666-8, SciTePress, pages 772-777. DOI: 10.5220/0012124600003555

in Bibtex Style

@conference{secrypt23,
author={Amani Bacha and Farah Barika Ktata and Faten Louati},
title={Improving Intrusion Detection Systems with Multi-Agent Deep Reinforcement Learning: Enhanced Centralized and Decentralized Approaches},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2023},
pages={772-777},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012124600003555},
isbn={978-989-758-666-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Improving Intrusion Detection Systems with Multi-Agent Deep Reinforcement Learning: Enhanced Centralized and Decentralized Approaches
SN - 978-989-758-666-8
AU - Bacha A.
AU - Barika Ktata F.
AU - Louati F.
PY - 2023
SP - 772
EP - 777
DO - 10.5220/0012124600003555
PB - SciTePress