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Paper Abstract and Keywords
Presentation 2026-03-19 10:40
Evaluation of Log Anomaly Detection Based Method on Log Message Content Using Large Language Models
Yoshie Morita, Kengo Tajiri, Yoichi Matsuo (NTT) ICM2025-37
Abstract (in Japanese) (See Japanese page) 
(in English) In log anomaly detection, deep learning-based methods that detect anomalies from sequential patterns, such as the order of log template occurrences, are widely used.
Existing methods have not fully utilized the semantic information in log messages, such as vocabulary and descriptive content. This may lead to the existence of anomalies that cannot be captured solely by sequential features.
In this study, log messages are transformed into embedding vectors using a large language model (LLM), and an anomaly score based on the Mahalanobis distance from the embedding distribution of the training data is introduced.
Furthermore, we propose an unsupervised anomaly detection method that integrates an anomaly score derived from LSTM-based prediction with an anomaly score based on LLM embeddings. We conducted a comparative evaluation using the public HDFS, OpenStack, and BGL datasets.
Keyword (in Japanese) (See Japanese page) 
(in English) log analysis, / anomaly detection / LSTM / LLM / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 422, ICM2025-37, pp. 17-22, March 2026.
Paper # ICM2025-37 
Date of Issue 2026-03-12 (ICM) 
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 ICM2025-37

Conference Information
Committee ICM  
Conference Date 2026-03-19 - 2026-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Hirarakou-Terminal Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ICM 
Conference Code 2026-03-ICM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of Log Anomaly Detection Based Method on Log Message Content Using Large Language Models 
Sub Title (in English)  
Keyword(1) log analysis,  
Keyword(2) anomaly detection  
Keyword(3) LSTM  
Keyword(4) LLM  
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1st Author's Name Yoshie Morita  
1st Author's Affiliation NTT Inc. (NTT)
2nd Author's Name Kengo Tajiri  
2nd Author's Affiliation NTT Inc. (NTT)
3rd Author's Name Yoichi Matsuo  
3rd Author's Affiliation NTT Inc. (NTT)
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Speaker Author-1 
Date Time 2026-03-19 10:40:00 
Presentation Time 25 minutes 
Registration for ICM 
Paper # ICM2025-37 
Volume (vol) vol.125 
Number (no) no.422 
Page pp.17-22 
#Pages 6 
Date of Issue 2026-03-12 (ICM) 

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