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Paper Abstract and Keywords
Presentation 2018-11-21 15:15
Malware Analysis Method Using Conditional Generative Adversarial Network
Keisuke Furumoto, Ryoichi Isawa, Takeshi Takahashi, Daisuke Inoue (NICT) ICSS2018-57
Abstract (in Japanese) (See Japanese page) 
(in English) Many schemes combining deep learning with methods for imaging malware have been proposed. These methods are considered to be statistical optimization problems unlike conventional manual analysis work. In the data set of malware, the naming rules and criteria of the correct label (malware family) are different for each security vendor, and there is a problem that the identification accuracy is greatly affected by the labeling method. Therefore, we consider approaches to apply Generative Adversarial Networks to malware analysis. In the GAN model, it is possible to learn feature quantities by unsupervised learning. In particular, this paper proposes a method using a model called Conditional GAN. The feature of the proposed method is that it is not necessary to associate correct labels with each specimen in a large-scale dataset, and label information from multiple security vendors can be used in the model.
Keyword (in Japanese) (See Japanese page) 
(in English) Malware / Deep Learning / Generative Adversarial Networks / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 315, ICSS2018-57, pp. 25-30, Nov. 2018.
Paper # ICSS2018-57 
Date of Issue 2018-11-14 (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)
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Conference Information
Committee ICSS  
Conference Date 2018-11-21 - 2018-11-22 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ICSS 
Conference Code 2018-11-ICSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Malware Analysis Method Using Conditional Generative Adversarial Network 
Sub Title (in English)  
Keyword(1) Malware  
Keyword(2) Deep Learning  
Keyword(3) Generative Adversarial Networks  
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1st Author's Name Keisuke Furumoto  
1st Author's Affiliation National Institute of Information and Communications Technology (NICT)
2nd Author's Name Ryoichi Isawa  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
3rd Author's Name Takeshi Takahashi  
3rd Author's Affiliation National Institute of Information and Communications Technology (NICT)
4th Author's Name Daisuke Inoue  
4th Author's Affiliation National Institute of Information and Communications Technology (NICT)
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Speaker Author-1 
Date Time 2018-11-21 15:15:00 
Presentation Time 25 minutes 
Registration for ICSS 
Paper # ICSS2018-57 
Volume (vol) vol.118 
Number (no) no.315 
Page pp.25-30 
#Pages 6 
Date of Issue 2018-11-14 (ICSS) 

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