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Paper Abstract and Keywords
Presentation 2026-03-17 10:50
Anomaly Detection in Network Traffic Using Gram Matrix Features and GM-VAE
Kazumasa Shimazu, Aoki Shigeki, Takao Miyamoto (Osaka Metropolitan Univ.) IT2025-120 ISEC2025-128 WBS2025-102 RCC2025-101
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the increasing sophistication of cyberattacks has heightened the importance of intrusion detection systems (IDS). While IDS approaches based on deep learning have employed Variational Autoencoders (VAE), VAE assumes a single Gaussian prior in the latent space, making it difficult to adequately represent multimodal data distributions such as those observed in network traffic. Moreover, when packet sequences are represented as images, texture-like characteristics arising from pixel-value distributions and repeated structural patterns become apparent; however, existing methods do not explicitly utilize such statistical structures. To address these issues, this study proposes an unsupervised anomaly detection method that extracts texture statistics from traffic images using Gram matrix features obtained from intermediate layers of VGG16, and models diverse normal communication behaviors as multiple latent clusters using a Gaussian Mixture VAE (GM-VAE). Experiments conducted on the CICIDS2017 dataset demonstrate the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Traffic image representation / Gram matrix features / GM-VAE / Unsupervised learning / Intrusion detection system (IDS) / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 405, ISEC2025-128, pp. 258-265, March 2026.
Paper # ISEC2025-128 
Date of Issue 2026-03-09 (IT, ISEC, WBS, RCC) 
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 IT2025-120 ISEC2025-128 WBS2025-102 RCC2025-101

Conference Information
Committee IT WBS ISEC RCC  
Conference Date 2026-03-16 - 2026-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagano Campus, Shinshu University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Joint Workshop of ISEC, IT, RCC, and WBS 
Paper Information
Registration To ISEC 
Conference Code 2026-03-IT-WBS-ISEC-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Anomaly Detection in Network Traffic Using Gram Matrix Features and GM-VAE 
Sub Title (in English)  
Keyword(1) Traffic image representation  
Keyword(2) Gram matrix features  
Keyword(3) GM-VAE  
Keyword(4) Unsupervised learning  
Keyword(5) Intrusion detection system (IDS)  
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1st Author's Name Kazumasa Shimazu  
1st Author's Affiliation Osaka Metropolitan University (Osaka Metropolitan Univ.)
2nd Author's Name Aoki Shigeki  
2nd Author's Affiliation Osaka Metropolitan University (Osaka Metropolitan Univ.)
3rd Author's Name Takao Miyamoto  
3rd Author's Affiliation Osaka Metropolitan University (Osaka Metropolitan Univ.)
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Speaker Author-1 
Date Time 2026-03-17 10:50:00 
Presentation Time 25 minutes 
Registration for ISEC 
Paper # IT2025-120, ISEC2025-128, WBS2025-102, RCC2025-101 
Volume (vol) vol.125 
Number (no) no.404(IT), no.405(ISEC), no.406(WBS), no.407(RCC) 
Page pp.258-265 
#Pages 8 
Date of Issue 2026-03-09 (IT, ISEC, WBS, RCC) 

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