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Paper Abstract and Keywords
Presentation 2019-11-02 15:00
On Robustness of Machine-Learning-Based Malware Detection
Wanjia Zheng (U. Tsukuba), Kazumasa Omote (U. Tsukuba/NICT) ISEC2019-83 SITE2019-77 LOIS2019-42
Abstract (in Japanese) (See Japanese page) 
(in English) As the 2020 Tokyo Olympics are approaching, the possibility of being targeted by attackers has further increased in Japan.
In order to prevent cyber attacks, machine learning technology that is spreading in various fields including images and voices recognition is also being used in the cyber security field.
However, there are many adversarial attack methods that trick machine learning models and cause it to malfunction.
In this study, we propose a robust malware detection model that is strong against adversarial attack methods combining dimensional compression and machine learning,
and evaluates robustness using PE file's information from FFRI Dataset 2018.
Measure the effectiveness and superiority of the proposed method from the viewpoint of robustness other than detection accuracy by measuring the distance(Euclidean distance between centroids, etc.) between malware and normal file data and evaluating the success rate of the attack method (SVM-Attack) is shown in this study.
Keyword (in Japanese) (See Japanese page) 
(in English) Malware Detection / Machine Learning / Dimension Reduction / Adversarial Attack / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 257, ISEC2019-83, pp. 133-140, Nov. 2019.
Paper # ISEC2019-83 
Date of Issue 2019-10-25 (ISEC, SITE, LOIS) 
ISSN Print edition: ISSN 0913-5685    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 ISEC2019-83 SITE2019-77 LOIS2019-42

Conference Information
Committee ISEC SITE LOIS  
Conference Date 2019-11-01 - 2019-11-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ISEC 
Conference Code 2019-11-ISEC-SITE-LOIS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On Robustness of Machine-Learning-Based Malware Detection 
Sub Title (in English)  
Keyword(1) Malware Detection  
Keyword(2) Machine Learning  
Keyword(3) Dimension Reduction  
Keyword(4) Adversarial Attack  
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Keyword(6)  
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1st Author's Name Wanjia Zheng  
1st Author's Affiliation University of Tsukuba (U. Tsukuba)
2nd Author's Name Kazumasa Omote  
2nd Author's Affiliation University of Tsukuba (U. Tsukuba/NICT)
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Speaker Author-1 
Date Time 2019-11-02 15:00:00 
Presentation Time 25 minutes 
Registration for ISEC 
Paper # ISEC2019-83, SITE2019-77, LOIS2019-42 
Volume (vol) vol.119 
Number (no) no.257(ISEC), no.258(SITE), no.259(LOIS) 
Page pp.133-140 
#Pages 8 
Date of Issue 2019-10-25 (ISEC, SITE, LOIS) 

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