Paper Abstract and Keywords |
Presentation |
2020-12-11 10:20
A Preliminary Multi-Agent Reinforcement Learning Approach for Responding Dynamic Traffic in Communication Destination Anonymization Keita Sugiyama, Naoki Fukuta (Shizuoka Univ.) AI2020-10 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we describe our prototype mechanism using the simulation-based multi-agent reinforcement learning for automatically allocating resources for anonymizing communication destinations as one of the applications of the multi-agent techniques for network virtualization. As an example of concrete scenarios, we assume a network where end-hosts are connected frequently and traffic trends change frequently for this reason. In this scenario, we implement a mechanism that allows multiple agents represented by network switches to cooperate with other agents autonomously for adjusting the level of anonymity using multi-agent reinforcement learning, and confirm the effect by simulation. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Multi-Agent Reinforcement Learning / Moving Target Defense / Network Security / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 281, AI2020-10, pp. 46-51, Dec. 2020. |
Paper # |
AI2020-10 |
Date of Issue |
2020-12-03 (AI) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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AI2020-10 |
Conference Information |
Committee |
AI |
Conference Date |
2020-12-10 - 2020-12-10 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online and HAMAMATSU ACT CITY |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Foundations and application technologies for AI systems on the new normal |
Paper Information |
Registration To |
AI |
Conference Code |
2020-12-AI |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Preliminary Multi-Agent Reinforcement Learning Approach for Responding Dynamic Traffic in Communication Destination Anonymization |
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Multi-Agent Reinforcement Learning |
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Moving Target Defense |
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Network Security |
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1st Author's Name |
Keita Sugiyama |
1st Author's Affiliation |
Shizuoka University (Shizuoka Univ.) |
2nd Author's Name |
Naoki Fukuta |
2nd Author's Affiliation |
Shizuoka University (Shizuoka Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-12-11 10:20:00 |
Presentation Time |
25 minutes |
Registration for |
AI |
Paper # |
AI2020-10 |
Volume (vol) |
vol.120 |
Number (no) |
no.281 |
Page |
pp.46-51 |
#Pages |
6 |
Date of Issue |
2020-12-03 (AI) |
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