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Paper Abstract and Keywords
Presentation 2025-01-24 11:45
A Phishing Site Detection Method through Analysis of HTML Code Using Large Language Models
Kairi Okada, Minami Yoda, Yutaka Matsuno (Nihon Univ.) KBSE2024-51
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the damages caused by phishing scams have sharply increased in Japan, surging approximately twentyfold between 2019 and 2023. Although various phishing detection methods have been proposed, the number of victims has not declined. Among existing research, one study proposed a phishing detection system utilizing large language models (LLM) by leveraging images, domain information, and partially extracted HTML. Their analysis indicated that misclassifications were primarily due to domain-related factors and social engineering (SE) techniques, while the impact of HTML simplification was not examined. Consequently, there is a possibility that the simplified HTML had only a minimal impact on phishing detection and might be insufficient as input data. To address this, our study attempts phishing detection without simplifying the HTML, aiming to improve accuracy by including phishing elements embedded within the original HTML source. Through experiments on a subset of data previously misclassified in existing research, we confirmed that LLMs can indeed utilize the full HTML source as a judgment basis for phishing detection.
Keyword (in Japanese) (See Japanese page) 
(in English) Phishing Sites / Social Engineering / Large Language Models / HTML / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 358, KBSE2024-51, pp. 75-80, Jan. 2025.
Paper # KBSE2024-51 
Date of Issue 2025-01-16 (KBSE) 
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 KBSE2024-51

Conference Information
Committee KBSE  
Conference Date 2025-01-23 - 2025-01-24 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka Kyoiku University（Tennoji） 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To KBSE 
Conference Code 2025-01-KBSE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Phishing Site Detection Method through Analysis of HTML Code Using Large Language Models 
Sub Title (in English)  
Keyword(1) Phishing Sites  
Keyword(2) Social Engineering  
Keyword(3) Large Language Models  
Keyword(4) HTML  
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1st Author's Name Kairi Okada  
1st Author's Affiliation Nihon University (Nihon Univ.)
2nd Author's Name Minami Yoda  
2nd Author's Affiliation Nihon University (Nihon Univ.)
3rd Author's Name Yutaka Matsuno  
3rd Author's Affiliation Nihon University (Nihon Univ.)
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Speaker Author-1 
Date Time 2025-01-24 11:45:00 
Presentation Time 30 minutes 
Registration for KBSE 
Paper # KBSE2024-51 
Volume (vol) vol.124 
Number (no) no.358 
Page pp.75-80 
#Pages 6 
Date of Issue 2025-01-16 (KBSE) 

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