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Paper Abstract and Keywords
Presentation 2021-03-02 11:05
Accuracy evaluation of CNNs and random forests with Malware API call sequences
Shugo Asai, Futa Yuichi (TUT) ICSS2020-58
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we compare the accuracy of the results of classifying each malware family by Random Forest and CNN using the preprocessed data generated by extracting only three characteristic word groups from API calls.
Keyword (in Japanese) (See Japanese page) 
(in English) Malware / API call / CNN / random forest / classification / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 384, ICSS2020-58, pp. 190-194, March 2021.
Paper # ICSS2020-58 
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 ICSS2020-58

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) Accuracy evaluation of CNNs and random forests with Malware API call sequences 
Sub Title (in English)  
Keyword(1) Malware  
Keyword(2) API call  
Keyword(3) CNN  
Keyword(4) random forest  
Keyword(5) classification  
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1st Author's Name Shugo Asai  
1st Author's Affiliation Tokyo University of Technology (TUT)
2nd Author's Name Futa Yuichi  
2nd Author's Affiliation Tokyo University of Technology (TUT)
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Speaker Author-1 
Date Time 2021-03-02 11:05:00 
Presentation Time 25 minutes 
Registration for ICSS 
Paper # ICSS2020-58 
Volume (vol) vol.120 
Number (no) no.384 
Page pp.190-194 
#Pages 5 
Date of Issue 2021-02-22 (ICSS) 

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