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Paper Abstract and Keywords
Presentation 2025-07-31 11:10
Traffic Analysis based on Periodicity Analysis using Graphs for Device Type Classification
Chikako Takasaki, Tomohiro Korikawa, Takaaki Moriya, Kyota Hattori (NTT) NS2025-60
Abstract (in Japanese) (See Japanese page) 
(in English) In the beyond 5G and 6G networks, the number of connected devices and their types of services will greatly increase including not only user devices such as smartphones but also the Internet of Things (IoT). Therefore, the existing common control for all devices cannot fulfill the network requirements of each device; however, it is not feasible to control networks per device in terms of computational complexity. We introduce device types as groups of devices with similar traffic characteristics to control network per device type for efficient network control. We present a method to classify device types based on periodicity analysis of only encrypted traffic behavior. In real networks, feature engineering, such as manual feature selection and parameter optimization depending on input traffic, is not feasible because operators must treat a large amoount of traffic. This paper proposes a graph-based traffic classification method that trains only two-dimensional features: packet inter-arrival times and packet sizes. The proposed method extracts the typical length of the traffic, which is different depending on the input traffic, using periodicity analysis. The traffic for the extracted length is converted into graphs whose two adjacent nodes have an edge, which is defined as textit{linear graphs} in this paper. The inter-arrival times and the packet sizes are represented as node features and edge weights, respectively.
The evaluation results show that the proposed method maintains 95-99% accuracy even trained with two-dimensional features, which is less than the numbers of features used in the existing methods, for two open datasets. Therefore, the proposed method is expected to reduce the feature engineering while maintaining the accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) device type classification / traffic analysis / periodicity analysis / graph neural network / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 132, NS2025-60, pp. 40-45, July 2025.
Paper # NS2025-60 
Date of Issue 2025-07-23 (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 NS2025-60

Conference Information
Committee SR RCS RCC SeMI NS RISING HCL  
Conference Date 2025-07-30 - 2025-08-01 
Place (in Japanese) (See Japanese page) 
Place (in English) MALIOS 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NS 
Conference Code 2025-07-SR-RCS-RCC-SeMI-NS-RISING-HCL 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Traffic Analysis based on Periodicity Analysis using Graphs for Device Type Classification 
Sub Title (in English)  
Keyword(1) device type classification  
Keyword(2) traffic analysis  
Keyword(3) periodicity analysis  
Keyword(4) graph neural network  
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1st Author's Name Chikako Takasaki  
1st Author's Affiliation NTT (NTT)
2nd Author's Name Tomohiro Korikawa  
2nd Author's Affiliation NTT (NTT)
3rd Author's Name Takaaki Moriya  
3rd Author's Affiliation NTT (NTT)
4th Author's Name Kyota Hattori  
4th Author's Affiliation NTT (NTT)
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Speaker Author-1 
Date Time 2025-07-31 11:10:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2025-60 
Volume (vol) vol.125 
Number (no) no.132 
Page pp.40-45 
#Pages
Date of Issue 2025-07-23 (NS) 


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