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Paper Abstract and Keywords
Presentation 2026-03-03 15:15
Evaluating the Generalizability of a Two-Stage Classification-Based Intrusion Detection Model for Unknown Attacks
Masako Miyazawa, Kazutaka Matsuzaki (Chuo Univ.) SITE2025-82 IA2025-81
Abstract (in Japanese) (See Japanese page) 
(in English) A two-stage classification-based intrusion detection model for MQTT brokers that combines a one-class classifier based on Isolation Forest and a multi-class classifier based on a decision tree has been proposed in prior work. The model distinguishes unknown attacks from known traffic in the first stage, and classifies known traffic into benign traffic and each known attack class in the second stage. In this paper, we apply the proposed model to the CSE-CIC IDS2018 dataset based on flow features, and quantitatively evaluate its performance using metrics for each stage as well as end-to-end metrics for the overall full pipeline. The results confirm that the proposed model can be transferred to another dataset. However, there remains room for improvement in generalization performance, and in particular, the detector performance in the first stage is shown to largely determine the overall performance. In addition, we construct features focusing on infinite values and features based on time-window aggregation, and compare their impact on generalization performance across feature settings. The results indicate a trade-off between unknown attack detection and multi-class classification of known attacks, and the effectiveness of the added features tends to depend on the type of unknown attack.
Keyword (in Japanese) (See Japanese page) 
(in English) intrusion detection system / CSE-CIC-IDS2018 / two-stage classification / unknown attack detection / generalization evaluation / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 379, IA2025-81, pp. 33-38, March 2026.
Paper # IA2025-81 
Date of Issue 2026-02-24 (SITE, IA) 
ISSN Online edition: ISSN 2432-6380
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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 SITE2025-82 IA2025-81

Conference Information
Committee IA SITE IPSJ-IOT  
Conference Date 2026-03-03 - 2026-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet and Information Ethics Education, etc. 
Paper Information
Registration To IA 
Conference Code 2026-03-IA-SITE-IOT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluating the Generalizability of a Two-Stage Classification-Based Intrusion Detection Model for Unknown Attacks 
Sub Title (in English)  
Keyword(1) intrusion detection system  
Keyword(2) CSE-CIC-IDS2018  
Keyword(3) two-stage classification  
Keyword(4) unknown attack detection  
Keyword(5) generalization evaluation  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Masako Miyazawa  
1st Author's Affiliation Chuo University (Chuo Univ.)
2nd Author's Name Kazutaka Matsuzaki  
2nd Author's Affiliation Chuo University (Chuo Univ.)
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Speaker Author-1 
Date Time 2026-03-03 15:15:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # SITE2025-82, IA2025-81 
Volume (vol) vol.125 
Number (no) no.378(SITE), no.379(IA) 
Page pp.33-38 
#Pages
Date of Issue 2026-02-24 (SITE, IA) 


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