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Paper Abstract and Keywords
Presentation 2025-03-07 13:50
Initial Evaluation of Asynchronous Federated Learning for Intrusion Detection Based on Multipoint Gateway Cooperation
Mao Kawaguchi, Takumi Miyoshi, Taku Yamazaki (Shibaura Inst. Tech.), Masataka Nakahara, Norihiro Okui, Ayumu Kubota (KDDI Research) NS2024-255
Abstract (in Japanese) (See Japanese page) 
(in English) With the widespread adoption of internet of things (IoT) devices, attacks targeting these devices have been in- creasing. Therefore, appropriate countermeasures for such unknown and diverse attacks on IoT devices are required.However, However, many IoT devices have limited computational resources, and their firmware updates for security issues depend on their vendors, making it challenging to implement security measures on each device. To address this issue, a collaborative intrusion detection method among gateways deployed at multiple points based on federated learning has been proposed. However, this method requires synchronous learning among all gateways as clients in the training phase. Consequently, if a gateway failure or communication failure between a gateway and the server occurs, the overall system processing may experience delays. In this paper, we propose an intrusion detection method based on asynchronous federated learning using decision tree learning. Additionally, we implement the proposed method and verify its asynchronous operation and processing time.
Keyword (in Japanese) (See Japanese page) 
(in English) Internet of things / machine learning / federated learning / intrusion detection / decision tree / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 419, NS2024-255, pp. 351-354, March 2025.
Paper # NS2024-255 
Date of Issue 2025-02-27 (NS) 
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)
Download PDF NS2024-255

Conference Information
Committee IN NS  
Conference Date 2025-03-06 - 2025-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2025-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Initial Evaluation of Asynchronous Federated Learning for Intrusion Detection Based on Multipoint Gateway Cooperation 
Sub Title (in English)  
Keyword(1) Internet of things  
Keyword(2) machine learning  
Keyword(3) federated learning  
Keyword(4) intrusion detection  
Keyword(5) decision tree  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Mao Kawaguchi  
1st Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. Tech.)
2nd Author's Name Takumi Miyoshi  
2nd Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. Tech.)
3rd Author's Name Taku Yamazaki  
3rd Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. Tech.)
4th Author's Name Masataka Nakahara  
4th Author's Affiliation KDDI Research, Inc. (KDDI Research)
5th Author's Name Norihiro Okui  
5th Author's Affiliation KDDI Research, Inc. (KDDI Research)
6th Author's Name Ayumu Kubota  
6th Author's Affiliation KDDI Research, Inc. (KDDI Research)
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Speaker Author-1 
Date Time 2025-03-07 13:50:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2024-255 
Volume (vol) vol.124 
Number (no) no.419 
Page pp.351-354 
#Pages 4 
Date of Issue 2025-02-27 (NS) 

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