Paper Abstract and Keywords |
Presentation |
2017-11-21 10:55
Subspecies Classification for Malwares based on Visualization of Dynamic Analysis result Taiki Gouda, Korehito Kashiki, Keisuke Furumoto, Masakatu Morii (Kobe Univ.) ICSS2017-45 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Along with the activation of malware infection activity, the appearance frequency of new subspecies also tends to increase. Research is being actively conducted to efficiently classify malware as a subspecies by using dynamic analysis results without analyzing malware in detail. Several classification methods focusing on the appearance frequency of API calls have been proposed so far, but binary classification using SVM is used as a criterion for evaluation. In this paper, we propose a method of classifying malware by imaging according to the frequency of appearance of API calls and using Deep Learning. Unlike conventional research, we constructed a neural network using learning data set consisting of multiple subspecies, and evaluated in multivalued classification from multiple choices. We show that multivalued classification of subspecies of malware can be realized by using the proposed method. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Malware / Subclassification / Visualization / Deep Learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 316, ICSS2017-45, pp. 41-45, Nov. 2017. |
Paper # |
ICSS2017-45 |
Date of Issue |
2017-11-13 (ICSS) |
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) |
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ICSS2017-45 |
Conference Information |
Committee |
ICSS |
Conference Date |
2017-11-20 - 2017-11-21 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Beppu International Convention Center |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Information Communication System Security, etc. |
Paper Information |
Registration To |
ICSS |
Conference Code |
2017-11-ICSS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Subspecies Classification for Malwares based on Visualization of Dynamic Analysis result |
Sub Title (in English) |
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Keyword(1) |
Malware |
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Subclassification |
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Visualization |
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Deep Learning |
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1st Author's Name |
Taiki Gouda |
1st Author's Affiliation |
Kobe University (Kobe Univ.) |
2nd Author's Name |
Korehito Kashiki |
2nd Author's Affiliation |
Kobe University (Kobe Univ.) |
3rd Author's Name |
Keisuke Furumoto |
3rd Author's Affiliation |
Kobe University (Kobe Univ.) |
4th Author's Name |
Masakatu Morii |
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Kobe University (Kobe Univ.) |
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Speaker |
Author-1 |
Date Time |
2017-11-21 10:55:00 |
Presentation Time |
25 minutes |
Registration for |
ICSS |
Paper # |
ICSS2017-45 |
Volume (vol) |
vol.117 |
Number (no) |
no.316 |
Page |
pp.41-45 |
#Pages |
5 |
Date of Issue |
2017-11-13 (ICSS) |
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