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Paper Abstract and Keywords
Presentation 2025-01-23 11:35
Device Type Classification based on Traffic Analysis Using Graph
Chikako Takasaki, Tomohiro Korikawa, Kyota Hattori (NTT) NS2024-173
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 [1], [2]. In real networks, the method has a problem that the preprocessing time increases because the periodicity analysis and feature extraction of the method have high computational complexity. This paper proposes a method to convert network traffic into graphs focusing on the inter-arrival times among packets. We construct the path graphs by representing the packets as the nodes and the time series of the packets as the edges. The evaluation results show that the proposed method reduces the processing time for converting traffic into graph format features to be input into the classification model and maintains the classification accuracy. We also discuss the accuracies when training with the different amount of traffic.
Keyword (in Japanese) (See Japanese page) 
(in English) ｄevice classification / traffic analysis / graph classification / machine learning / deep learning / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 344, NS2024-173, pp. 19-24, Jan. 2025.
Paper # NS2024-173 
Date of Issue 2025-01-16 (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 NS2024-173

Conference Information
Committee NS NWS  
Conference Date 2025-01-23 - 2025-01-24 
Place (in Japanese) (See Japanese page) 
Place (in English) Higashi-Yodogawa Community Center + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network software (Software architecture, Middleware), Network application, SOA/SDP, NGN/IMS/API, Distributed control/Dynamic routing, Grid, NFV, IoT, Network/System reliability, Network/System evaluation, etc. 
Paper Information
Registration To NS 
Conference Code 2025-01-NS-NWS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Device Type Classification based on Traffic Analysis Using Graph 
Sub Title (in English)  
Keyword(1) ｄevice classification  
Keyword(2) traffic analysis  
Keyword(3) graph classification  
Keyword(4) machine learning  
Keyword(5) deep learning  
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Keyword(7)  
Keyword(8)  
1st Author's Name Chikako Takasaki  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Tomohiro Korikawa  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Kyota Hattori  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2025-01-23 11:35:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2024-173 
Volume (vol) vol.124 
Number (no) no.344 
Page pp.19-24 
#Pages 6 
Date of Issue 2025-01-16 (NS) 

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