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Paper Abstract and Keywords
Presentation 2022-03-10 11:00
Experimental Evaluation of Influence of Distributing Deep Learning-Based IDSs on Their Classification Accuracy and Explainability
Ayaka Oki, Yukio Ogawa, Kaoru Ota, Mianxiong Dong (Muroran-IT) IN2021-33
Abstract (in Japanese) (See Japanese page) 
(in English) Increased data traffic associated with the wide spread usage of IoT devices accentuates the risk of large-scale cyber attacks in the future. Intrusion detection systems (IDSs) thus need to be distributed in the edge computing for defending the attacks in parallel. The adoption of machine learning and eXplainable Artificial Intelligence (XAI) can improve the accuracy and reasoning estimation of IDSs, but the influence of the distribution on them are not clarified. We therefore simulate a distributed IDS and evaluate the influence on each attack category by decreasing the amount of training data given to the IDS. Our evaluations show that the accuracy decreases when the number of distributed IDSs is more than 100 and the precision also decreases by 10% to 30%. This is not only due to the lack of training data, but also the fact that the evidence features used for reasoning estimation have a higher similarity among different attack categories.
Keyword (in Japanese) (See Japanese page) 
(in English) distributed intrusion detection system / machine learning / explainable artificial intelligence / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 434, IN2021-33, pp. 13-18, March 2022.
Paper # IN2021-33 
Date of Issue 2022-03-03 (IN) 
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 IN2021-33

Conference Information
Committee NS IN  
Conference Date 2022-03-10 - 2022-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To IN 
Conference Code 2022-03-NS-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Experimental Evaluation of Influence of Distributing Deep Learning-Based IDSs on Their Classification Accuracy and Explainability 
Sub Title (in English)  
Keyword(1) distributed intrusion detection system  
Keyword(2) machine learning  
Keyword(3) explainable artificial intelligence  
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1st Author's Name Ayaka Oki  
1st Author's Affiliation Muroran Institute of Technology (Muroran-IT)
2nd Author's Name Yukio Ogawa  
2nd Author's Affiliation Muroran Institute of Technology (Muroran-IT)
3rd Author's Name Kaoru Ota  
3rd Author's Affiliation Muroran Institute of Technology (Muroran-IT)
4th Author's Name Mianxiong Dong  
4th Author's Affiliation Muroran Institute of Technology (Muroran-IT)
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Speaker Author-1 
Date Time 2022-03-10 11:00:00 
Presentation Time 20 minutes 
Registration for IN 
Paper # IN2021-33 
Volume (vol) vol.121 
Number (no) no.434 
Page pp.13-18 
#Pages
Date of Issue 2022-03-03 (IN) 


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