loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Hoang Viet Nguyen 1 ; Songpon Teerakanok 2 ; Atsuo Inomata 3 and Tetsutaro Uehara 1

Affiliations: 1 Cyber Security Lab, College of Information Science and Engineering, Ritsumeikan University, Japan ; 2 Research Organization of Science and Technology, Ritsumeikan University, Japan ; 3 Graduate School of Information Science and Technology, Osaka University, Japan

Keyword(s): Penetration Testing, Deep Reinforcement Learning.

Abstract: Reinforcement learning (RL) is a widely used machine learning method for optimal decision-making compared to rule-based methods. Because of that advantage, RL has also recently been used a lot in penetration testing (PT) problems to assist in planning and deploying cyber attacks. Although the complexity and size of networks keep increasing vastly every day, RL is currently applied only for small scale networks. This paper proposes a double agent architecture (DAA) approach that is able to drastically increase the size of the network which can be solved with RL. This work also examines the effectiveness of using current popular deep reinforcement learning algorithms including DQN, DDQN, Dueling DQN and D3QN algorithms for PT. The A2C algorithm using Wolpertinger architecture is also adopted as a baseline for comparing the results of the methods. All algorithms are evaluated using a proposed network simulator which is constructed as a Markov decision process (MDP). Our results demonstr ate that DAA with A2C algorithm far outweighs other approaches when dealing with large network environments reaching up to 1000 hosts. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.222.67.251

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Nguyen, H.; Teerakanok, S.; Inomata, A. and Uehara, T. (2021). The Proposal of Double Agent Architecture using Actor-critic Algorithm for Penetration Testing. In Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-491-6; ISSN 2184-4356, SciTePress, pages 440-449. DOI: 10.5220/0010232504400449

@conference{icissp21,
author={Hoang Viet Nguyen. and Songpon Teerakanok. and Atsuo Inomata. and Tetsutaro Uehara.},
title={The Proposal of Double Agent Architecture using Actor-critic Algorithm for Penetration Testing},
booktitle={Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP},
year={2021},
pages={440-449},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010232504400449},
isbn={978-989-758-491-6},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Information Systems Security and Privacy - ICISSP
TI - The Proposal of Double Agent Architecture using Actor-critic Algorithm for Penetration Testing
SN - 978-989-758-491-6
IS - 2184-4356
AU - Nguyen, H.
AU - Teerakanok, S.
AU - Inomata, A.
AU - Uehara, T.
PY - 2021
SP - 440
EP - 449
DO - 10.5220/0010232504400449
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic