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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 |
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| 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 | 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 | NS2019-121 |
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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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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 |
| #Pages | 6 |
| Date of Issue | 2019-11-14 (NS) |
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