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Paper Abstract and Keywords
Presentation 2026-03-04 11:35
Temporal Super-Resolution of Network Traffic Data Using Transformer Models
Takeshi Goto, Ryo Yamamoto, Satoshi Ohzahata (UEC) NS2025-224
Abstract (in Japanese) (See Japanese page) 
(in English) Network traffic data collected via protocols such as SNMP is essential for understanding network usage and detecting anomalies.
Typically, a monitoring server polls network devices at fixed intervals to collect this data.
However, while network devices are capable of high-speed packet forwarding, their performance in data collection is often limited.
Consequently, coarse-grained sampling is often necessary.
This coarse granularity results in the loss of high-frequency components, which makes it difficult to accurately capture temporal changes in traffic.
In this paper, we propose a method that utilizes a Transformer-based deep learning model to super-resolve coarse-grained traffic data into fine-grained data.
Our approach focuses on the spatial correlations of traffic data between network devices and learns these relationships using the Transformer.
Furthermore, we apply proposed hard constraints and a spectral loss function to the model's output to train it to produce more plausible distributions.
This approach enables fine-grained situational awareness and minimizes the load on network devices.
Performance evaluations demonstrate that the proposed method achieves higher accuracy in traffic data generation compared to existing Transformer-based techniques.
Keyword (in Japanese) (See Japanese page) 
(in English) Network Telemetry / Temporal Super-Resolution / Deep learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 385, NS2025-224, pp. 19-24, March 2026.
Paper # NS2025-224 
Date of Issue 2026-02-25 (NS) 
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 NS2025-224

Conference Information
Committee IN NS  
Conference Date 2026-03-04 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa-Ken Shichoson Jichi Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2026-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Temporal Super-Resolution of Network Traffic Data Using Transformer Models 
Sub Title (in English)  
Keyword(1) Network Telemetry  
Keyword(2) Temporal Super-Resolution  
Keyword(3) Deep learning  
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1st Author's Name Takeshi Goto  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Ryo Yamamoto  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Satoshi Ohzahata  
3rd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2026-03-04 11:35:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2025-224 
Volume (vol) vol.125 
Number (no) no.385 
Page pp.19-24 
#Pages 6 
Date of Issue 2026-02-25 (NS) 

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