IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2020-07-17 09:25
Study on which data we should label in a few-shot learning for service identification over encrypted web services
Shouta Yoshida, Yutaka Eguchi, Kohei Shiomoto (TCU) ICM2020-14
Abstract (in Japanese) (See Japanese page) 
(in English) It is very important to monitor and control the communication traffic to cope with the
increasing communication traffic.However, due to the recent standardization of encryption, it is not possible to distinguish the type of traffic.Therefore, in this paper, we use the observable packet data as a feature for encrypted communications. Using the supervised learning method, Few-shot Learning, to describe the types of web services We propose a method for classification.The unlabeled dataset is dimensionally reduced by t-SNE and then k-means method to label the characteristic data near the center of gravity of the cluster. The method improves classification accuracy in a small number of data sets by 11%.
Keyword (in Japanese) (See Japanese page) 
(in English) Traffic classification / Machine learning / Deep learning / Few-shot Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 109, ICM2020-14, pp. 37-42, July 2020.
Paper # ICM2020-14 
Date of Issue 2020-07-09 (ICM) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 ICM2020-14

Conference Information
Committee ICM  
Conference Date 2020-07-16 - 2020-07-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ICM 
Conference Code 2020-07-ICM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on which data we should label in a few-shot learning for service identification over encrypted web services 
Sub Title (in English)  
Keyword(1) Traffic classification  
Keyword(2) Machine learning  
Keyword(3) Deep learning  
Keyword(4) Few-shot Learning  
1st Author's Name Shouta Yoshida  
1st Author's Affiliation Tokyo City University (TCU)
2nd Author's Name Yutaka Eguchi  
2nd Author's Affiliation Tokyo City University (TCU)
3rd Author's Name Kohei Shiomoto  
3rd Author's Affiliation Tokyo City University (TCU)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Date Time 2020-07-17 09:25:00 
Presentation Time 25 
Registration for ICM 
Paper # IEICE-ICM2020-14 
Volume (vol) IEICE-120 
Number (no) no.109 
Page pp.37-42 
#Pages IEICE-6 
Date of Issue IEICE-ICM-2020-07-09 

[Return to Top Page]

[Return to IEICE Web Page]

The Institute of Electronics, Information and Communication Engineers (IEICE), Japan