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Paper Abstract and Keywords
Presentation 2025-03-07 15:10
Generating Adversarial Training Data for Specific Malware Evasion Using Reinforcement Learning with High-Dimensional Features and Continuous Action Space
Shota Kiba (Nagoya Univ.), Hirokazu Hasegawa (NII), Yukiko Yamaguchi, Hajime Shimada (Nagoya Univ.) ICSS2024-115
Abstract (in Japanese) (See Japanese page) 
(in English) The use of classifiers based on Machine Learning and Deep Learning is widely employed in the field of cybersecurity. However, there are numerous proposals that attack Machine Learning and Deep Learning based system so that the use of these technologies cannot be considered as entirely secure. In our laboratory, we have focused on the threat of poisoning attacks and promoted researches on poisoning attack data generation by combining GAN and reinforcement learning models, as well as the utilization of high-dimensional feature datasets. In this study, we advanced these prior studies to propose methods for creating specimens for poisoning attacks to verify the threats of targeted poisoning attacks. We improved upon previous research methods from three perspectives: the use of datasets with high-dimensional features, the utilization of reinforcement learning algorithms that support continuous action spaces, and the definition of weighted rewards, and evaluated their effectiveness.
Keyword (in Japanese) (See Japanese page) 
(in English) Malware Detection / Targeted Poisoning Attack / High-Dimensional Features / Continuous Action Space / Reinforcement Learning / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 422, ICSS2024-115, pp. 359-366, March 2025.
Paper # ICSS2024-115 
Date of Issue 2025-02-27 (ICSS) 
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 ICSS2024-115

Conference Information
Committee ICSS IPSJ-SPT  
Conference Date 2025-03-06 - 2025-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Prefectural Museum & Art Museum 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Security, Trust, etc. 
Paper Information
Registration To ICSS 
Conference Code 2025-03-ICSS-SPT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Generating Adversarial Training Data for Specific Malware Evasion Using Reinforcement Learning with High-Dimensional Features and Continuous Action Space 
Sub Title (in English)  
Keyword(1) Malware Detection  
Keyword(2) Targeted Poisoning Attack  
Keyword(3) High-Dimensional Features  
Keyword(4) Continuous Action Space  
Keyword(5) Reinforcement Learning  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Shota Kiba  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Hirokazu Hasegawa  
2nd Author's Affiliation National Institute of Infomatics (NII)
3rd Author's Name Yukiko Yamaguchi  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Hajime Shimada  
4th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2025-03-07 15:10:00 
Presentation Time 20 minutes 
Registration for ICSS 
Paper # ICSS2024-115 
Volume (vol) vol.124 
Number (no) no.422 
Page pp.359-366 
#Pages 8 
Date of Issue 2025-02-27 (ICSS) 

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