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Paper Abstract and Keywords
Presentation 2024-07-19 16:20
Feature Conversion based on Traffic Statistics Evaluation for Device Type Classification
Chikako Takasaki, Tomohiro Korikawa, Kyota Hattori (NTT) NS2024-73
Abstract (in Japanese) (See Japanese page) 
(in English) In the beyond 5G and 6G networks, the number of connected devices and their types will greatly increase including not only user devices such as smartphones but also the Internet of Things (IoT). Moreover, Non-terrestrial networks (NTN) introduce dynamic changes in the types of connected devices as base stations or access points are moving objects. Therefore, continuous network capacity design is required to fulfill the network requirements of each device.However, continuous optimization of network capacity design for each device within a short time span becomes difficult because of the heavy calculation amount. We introduce device types as groups of devices with similar traffic characteristics to optimize network capacity per device type for efficient network capacity design. A method has been proposed to classify device types based on periodicity analysis of only encrypted traffic behavior.In real networks, the method has the problem that the accuracy decreases when traffic is reconverted into waveforms along with changes in traffic behavior and dynamic MAC addresses.This paper proposes a method to convert network traffic into waveforms by extracting the most significant characteristic portion from traffic time series based on feature evaluation.The evaluation result shows that the proposed method improves overfitting and classification accuracy.We also discuss the correlation between the processing time of the proposed feature evaluation and the classification accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) device classification / traffic analysis / periodicity analysis / machine learning / deep learning / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 106, NS2024-73, pp. 158-163, July 2024.
Paper # NS2024-73 
Date of Issue 2024-07-10 (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 NS2024-73

Conference Information
Committee RCC RCS SeMI NS SR RISING  
Conference Date 2024-07-17 - 2024-07-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Citizens Activities Promotion Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NS 
Conference Code 2024-07-RCC-RCS-SeMI-NS-SR-RISING 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Feature Conversion based on Traffic Statistics Evaluation for Device Type Classification 
Sub Title (in English)  
Keyword(1) device classification  
Keyword(2) traffic analysis  
Keyword(3) periodicity analysis  
Keyword(4) machine learning  
Keyword(5) deep learning  
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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 2024-07-19 16:20:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2024-73 
Volume (vol) vol.124 
Number (no) no.106 
Page pp.158-163 
#Pages 6 
Date of Issue 2024-07-10 (NS) 

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