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Paper Abstract and Keywords
Presentation 2019-11-21 10:20
Visual Analytics for Anomaly Classification in LAN Based on Deep Convolutional Neural Network
Yuwei Sun, Hideya Ochiai, Hiroshi Esaki (UTokyo) NS2019-121
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, the attack monitored in Local Area Network (LAN) is surging. There are some methods being used to analyze the traffic data in LAN. However, the researches on how to visualize the feature of the events occurred in LAN from the traffic data are still not so many. With a broad spread of the concept of the Sustainable Development Goals (SDGs), the requirement of building a sustainable and smart city including the construction of a safe network is extremely imperative. In this paper, we propose visual analytics using a structure named Hilbert curve to generate feature maps representing some selected protocol information. As a brand-new approach, we merge all the protocol information based on the concept of a channel into one image. We collect the visualization result of several events to build a dataset. After this, we train a deep convolutional neural network (CNN), one kind of machine learning approaches based on the dataset. We evaluate the performance of our scheme using precision, recall, and F-measure in two active networks. The two networks have different purposes of usage. At last, we attain an average F-measure rate of around 0.76 in both networks.
Keyword (in Japanese) (See Japanese page) 
(in English) visualization / convolutional neural network / deep learning / network security / LAN / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 297, NS2019-121, pp. 7-12, Nov. 2019.
Paper # NS2019-121 
Date of Issue 2019-11-14 (NS) 
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)
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Conference Information
Committee NS ICM CQ NV  
Conference Date 2019-11-21 - 2019-11-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Rokkodai 2nd Campus, Kobe Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence, etc. 
Paper Information
Registration To NS 
Conference Code 2019-11-NS-ICM-CQ-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Visual Analytics for Anomaly Classification in LAN Based on Deep Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) visualization  
Keyword(2) convolutional neural network  
Keyword(3) deep learning  
Keyword(4) network security  
Keyword(5) LAN  
1st Author's Name Yuwei Sun  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Hideya Ochiai  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Hiroshi Esaki  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2019-11-21 10:20:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2019-121 
Volume (vol) vol.119 
Number (no) no.297 
Page pp.7-12 
Date of Issue 2019-11-14 (NS) 

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