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| Presentation | 2023-03-15 09:30 Networks anomaly detection by VAE based on features extracted by CNN Higashihata Kazuki (Osaka Prefecture Univ.), Aoki Shigeki, Miyamoto Takao (Osaka Metropolitan Univ.)  IT2022-111 ISEC2022-90 WBS2022-108 RCC2022-108 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Anomaly-based IDS, one of the intrusion detection systems (IDS), can detect unknown anomalies, but there is a problem of improving detection accuracy. In order to improve accuracy, methods using deep learning have been attracting attention. General deep learning methods require labeled data. However, it is difficult to obtain a wide variety of anomalies in the field of network security. Thus, we focus on Variational AutoEncoder (VAE), which is capable of unsupervised learning among deep learning methods. On the other hand, deep learning technology has made remarkable progress in the field of image processing, and methods using Convolurional Neural Network (CNN) have shown very high accuracy. Especially, features extracted from the middle layer of a pre-trained CNN on a large image dataset are applied to various tasks and there are researches that use such features for IDS. In this paper, we propose a method for highly accurate anomaly-based IDS that combines VAE and features extracted from a pre-trained CNN on a large image dataset. In our experiments, we confirmed the effectiveness of our method using CICIDS2017, BOS datasets, and traffic data acquired in a real environment. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | CNN / VAE / Anomaly Detection of Network / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 428, ISEC2022-90, pp. 269-276, March 2023. | |
| Paper # | ISEC2022-90 | |
| Date of Issue | 2023-03-07 (IT, ISEC, WBS, RCC) | |
| 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 | IT2022-111 ISEC2022-90 WBS2022-108 RCC2022-108 |
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| Conference Information | |
| Committee | RCC ISEC IT WBS |
| Conference Date | 2023-03-14 - 2023-03-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | ISEC |
| Conference Code | 2023-03-RCC-ISEC-IT-WBS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Networks anomaly detection by VAE based on features extracted by CNN |
| Sub Title (in English) | |
| Keyword(1) | CNN |
| Keyword(2) | VAE |
| Keyword(3) | Anomaly Detection of Network |
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| 1st Author's Name | Higashihata Kazuki |
| 1st Author's Affiliation | Osaka Prefecture University (Osaka Prefecture Univ.) |
| 2nd Author's Name | Aoki Shigeki |
| 2nd Author's Affiliation | Osaka Metropolitan University (Osaka Metropolitan Univ.) |
| 3rd Author's Name | Miyamoto Takao |
| 3rd Author's Affiliation | Osaka Metropolitan University (Osaka Metropolitan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-03-15 09:30:00 |
| Presentation Time | 25 minutes |
| Registration for | ISEC |
| Paper # | IT2022-111, ISEC2022-90, WBS2022-108, RCC2022-108 |
| Volume (vol) | vol.122 |
| Number (no) | no.427(IT), no.428(ISEC), no.429(WBS), no.430(RCC) |
| Page | pp.269-276 |
| #Pages | 8 |
| Date of Issue | 2023-03-07 (IT, ISEC, WBS, RCC) |
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