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Paper Abstract and Keywords
Presentation 2026-03-04 16:25
A Partial AUC Maximization Method Robust to Mislabeled Data and Overfitting for Security Log Analysis
Taishi Nishiyama (NTT), Kazunori Kamiya (NSH/NTT), Atsutoshi Kumagai (NTT) ICSS2025-138
Abstract (in Japanese) (See Japanese page) 
(in English) Log analysis with machine learning has become increasingly important for mitigating the impact of malware incidents. In actual security operations, analysts must identify as many suspicious logs as possible while suppressing false positives; therefore, improving the true positive rate (TPR) under low false positive rate (FPR) conditions is an important challenge. Motivated by this requirement, we focus on pAUC maximization learning, which directly maximizes the TPR within a specified FPR interval. However, when existing pAUC maximization methods are applied to a narrow FPR interval, classification performance may deteriorate significantly due to the relative shortage of training data and mislabeled samples. To alleviate these problems, we propose a method that improves detection performance in narrow FPR intervals by applying two pAUC maximization methods with different objectives depending on the mathematical characteristics of the features.
Keyword (in Japanese) (See Japanese page) 
(in English) Partial AUC / AUC / Log Analysis / Malware / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 381, ICSS2025-138, pp. 394-401, March 2026.
Paper # ICSS2025-138 
Date of Issue 2026-02-24 (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 ICSS2025-138

Conference Information
Committee ICSS IPSJ-SPT  
Conference Date 2026-03-03 - 2026-03-04 
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 2026-03-ICSS-SPT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Partial AUC Maximization Method Robust to Mislabeled Data and Overfitting for Security Log Analysis 
Sub Title (in English)  
Keyword(1) Partial AUC  
Keyword(2) AUC  
Keyword(3) Log Analysis  
Keyword(4) Malware  
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1st Author's Name Taishi Nishiyama  
1st Author's Affiliation NTT Social Informatics Laboratories (NTT)
2nd Author's Name Kazunori Kamiya  
2nd Author's Affiliation NTT Security Holdings/NTT Social Informatics Laboratories (NSH/NTT)
3rd Author's Name Atsutoshi Kumagai  
3rd Author's Affiliation NTT Social Informatics Laboratories (NTT)
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Speaker Author-1 
Date Time 2026-03-04 16:25:00 
Presentation Time 20 minutes 
Registration for ICSS 
Paper # ICSS2025-138 
Volume (vol) vol.125 
Number (no) no.381 
Page pp.394-401 
#Pages 8 
Date of Issue 2026-02-24 (ICSS) 

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