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Paper Abstract and Keywords
Presentation 2025-09-16 16:45
Improving Decoy-ify: A Decoy Platform Leveraging Large Language Models to Learn and Infer Fake Responses
Kazuma Kasahara (Keio Univ.), Takao Kondo (Hokkaido Univ.), Satoshi Kai, Satoru Tezuka (Keio Univ.) IA2025-23
Abstract (in Japanese) (See Japanese page) 
(in English) The Deception environment is a means of understanding and analyzing the trends in threat actors' activities that are highly relevant to one's own organization. A representative implementation approach is a cloud-based deception environment, such as STARDUST. However, a characteristic limitation of cloud-based deception environments is the difficulty in constructing deception settings that accurately reflect the risks inherent in sensitive information assets residing within the customer organization's network. To address this challenge, we previously proposed a hybrid deception architecture that combines on-premise decoys with a cloud-based deception environment. At its core, this approach included the implementation of a reverse proxy, ``Decoy-ify'', which transforms information assets into decoys, along with the fine-tuning of a large language model (LLM) for fake data generation. In this study, we enhance Decoy-ify to overcome the limitations identified in our prior work, focusing on ensuring confidentiality in response generation, generalizing response contents across corresponding decoy applications, and improving the versatility of decoy-based responses. Evaluation results confirmed that sensitive information is not leaked, while the use of declarative configuration files enables the generation of diverse formats of fake data. Furthermore, we demonstrated that the system possesses versatility in supporting multiple application formats, such as JSON, XML, and HTML.
Keyword (in Japanese) (See Japanese page) 
(in English) Cyber Security / Deception / Threat Intelligence / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 178, IA2025-23, pp. 44-51, Sept. 2025.
Paper # IA2025-23 
Date of Issue 2025-09-09 (IA) 
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 IA2025-23

Conference Information
Committee IA  
Conference Date 2025-09-16 - 2025-09-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet Operation and Management, Network Architecture, Communication Protocols, IoT, etc. 
Paper Information
Registration To IA 
Conference Code 2025-09-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving Decoy-ify: A Decoy Platform Leveraging Large Language Models to Learn and Infer Fake Responses 
Sub Title (in English)  
Keyword(1) Cyber Security  
Keyword(2) Deception  
Keyword(3) Threat Intelligence  
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1st Author's Name Kazuma Kasahara  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Takao Kondo  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Satoshi Kai  
3rd Author's Affiliation Keio University (Keio Univ.)
4th Author's Name Satoru Tezuka  
4th Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2025-09-16 16:45:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2025-23 
Volume (vol) vol.125 
Number (no) no.178 
Page pp.44-51 
#Pages 8 
Date of Issue 2025-09-09 (IA) 

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