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| Presentation | 2021-03-02 11:30 Detecting unknown malware families by anomaly detection using deep learning Ren Takeuchi, Shintaro Mizuno, Vo Ngoc Khoi Nguyen, Jun Tsuchiya, Masakatsu Nishigaki, Tetsushi Ohki (Shizuoka Univ.)  ICSS2020-59 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The analysis of malware has become an indispensable process for anti-malware. Malware is diversifying day by day, so that it is difficult to know whether a malware to be analyzed is a known malware or an unknown malware, and this is a hindrance to quick malware analysis. Therefore, in this paper, we propose a method to detect unknown malware families by extracting features from CNN trained only on known malware families and use those features for anomaly detection. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Malware / Anomaly Detection / Convolutional Neural Network / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 384, ICSS2020-59, pp. 195-200, March 2021. | |
| Paper # | ICSS2020-59 | |
| Date of Issue | 2021-02-22 (ICSS) | |
| 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 | ICSS2020-59 |
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| Conference Information | |
| Committee | ICSS IPSJ-SPT |
| Conference Date | 2021-03-01 - 2021-03-02 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Security, Trust, etc. |
| Paper Information | |
| Registration To | ICSS |
| Conference Code | 2021-03-ICSS-SPT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Detecting unknown malware families by anomaly detection using deep learning |
| Sub Title (in English) | |
| Keyword(1) | Malware |
| Keyword(2) | Anomaly Detection |
| Keyword(3) | Convolutional Neural Network |
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| 1st Author's Name | Ren Takeuchi |
| 1st Author's Affiliation | Shizuoka University (Shizuoka Univ.) |
| 2nd Author's Name | Shintaro Mizuno |
| 2nd Author's Affiliation | Shizuoka University (Shizuoka Univ.) |
| 3rd Author's Name | Vo Ngoc Khoi Nguyen |
| 3rd Author's Affiliation | Shizuoka University (Shizuoka Univ.) |
| 4th Author's Name | Jun Tsuchiya |
| 4th Author's Affiliation | Shizuoka University (Shizuoka Univ.) |
| 5th Author's Name | Masakatsu Nishigaki |
| 5th Author's Affiliation | Shizuoka University (Shizuoka Univ.) |
| 6th Author's Name | Tetsushi Ohki |
| 6th Author's Affiliation | Shizuoka University (Shizuoka Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-03-02 11:30:00 |
| Presentation Time | 25 minutes |
| Registration for | ICSS |
| Paper # | ICSS2020-59 |
| Volume (vol) | vol.120 |
| Number (no) | no.384 |
| Page | pp.195-200 |
| #Pages | 6 |
| Date of Issue | 2021-02-22 (ICSS) |
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