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Paper Abstract and Keywords
Presentation 2021-02-12 16:30
A Defense Method for Machine Learning Poisoning Attacks in IoT Environments Considering the Removal Priority of Poisonous Data
Tomoki Chiba, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga (UEC) AI2020-36
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, machine learning has been attracting attention for its potential to further enrich people's lives. However, this has been accompanied by an increase in the number of vulnerabilities in systems that use machine learning. One such threat is the poisoning attack, which introduces poisonous data into the training data used to build machine learning models. The goal of this attack is to reduce the accuracy of the machine learning model or to output the prediction results that the attacker intended. In this paper, we propose a defense method to reduce the accuracy degradation of machine learning models caused by poisoning attacks. There are various scenarios for constructing machine learning models, but in this study, we assume an IoT environment, in which there are multiple sources of data, and an attacker may hide in one of them. In this study, we define a trust level for each source of data using poisonous data used in poisoning attacks, and remove data according to the trust level to suppress the accuracy degradation caused by poisoning attacks. In the evaluation experiments of the proposed method in this study, the detection accuracy of the proposed method is 80%, which is up to 50% higher than the accuracy of existing method.
Keyword (in Japanese) (See Japanese page) 
(in English) machine learning / security / IoT / poisoning / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 362, AI2020-36, pp. 73-78, Feb. 2021.
Paper # AI2020-36 
Date of Issue 2021-02-05 (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)
Download PDF AI2020-36

Conference Information
Committee AI  
Conference Date 2021-02-12 - 2021-02-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To AI 
Conference Code 2021-02-AI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Defense Method for Machine Learning Poisoning Attacks in IoT Environments Considering the Removal Priority of Poisonous Data 
Sub Title (in English)  
Keyword(1) machine learning  
Keyword(2) security  
Keyword(3) IoT  
Keyword(4) poisoning  
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1st Author's Name Tomoki Chiba  
1st Author's Affiliation University of Electro-Communications (UEC)
2nd Author's Name Yuichi Sei  
2nd Author's Affiliation University of Electro-Communications (UEC)
3rd Author's Name Yasuyuki Tahara  
3rd Author's Affiliation University of Electro-Communications (UEC)
4th Author's Name Akihiko Ohsuga  
4th Author's Affiliation University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2021-02-12 16:30:00 
Presentation Time 20 minutes 
Registration for AI 
Paper # AI2020-36 
Volume (vol) vol.120 
Number (no) no.362 
Page pp.73-78 
#Pages 6 
Date of Issue 2021-02-05 (AI) 

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