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Paper Abstract and Keywords
Presentation 2024-09-05 15:50
Malware detection using LC-KSVD with Sparse Random Projection
Aziz Urahman Shafaq, Takayuki Nakachi (Univ. of the Ryukyus) LOIS2024-25 IE2024-31 EMM2024-81
Abstract (in Japanese) (See Japanese page) 
(in English) Malware detection is increasingly challenging due to the sophistication of threats. This study presents a small data-driven malware detection approach that integrates Label Consistent K-SVD (LC-KSVD) with Sparse Random Projection (SRP). SRP effectively reduces the dimensionality of high-dimensional malware image data while maintaining its core structure, and LC-KSVD, which incorporates label information, refines dictionary learning for better classification. The unified objective function, optimized using K-SVD, combines reconstruction and classification errors. The approach is evaluated based on classification accuracy, and computational complexity. Simulation results demonstrate that LC-KSVD with SRP achieves better classification accuracy compared to LC-KSVD with traditional dimensionality reduction methods, potentially advancing the field of malware detection and cybersecurity.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine Learning / Malware Detection / LC-KSVD / Sparse Random Projection / Malimg Dataset / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 177, EMM2024-81, pp. 71-76, Sept. 2024.
Paper # EMM2024-81 
Date of Issue 2024-08-28 (LOIS, IE, EMM) 
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 LOIS2024-25 IE2024-31 EMM2024-81

Conference Information
Committee EMM ITE-ME IE LOIS IEE-CMN IPSJ-AVM  
Conference Date 2024-09-04 - 2024-09-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EMM 
Conference Code 2024-09-EMM-ME-IE-LOIS-CMN-AVM 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Malware detection using LC-KSVD with Sparse Random Projection 
Sub Title (in English)  
Keyword(1) Machine Learning  
Keyword(2) Malware Detection  
Keyword(3) LC-KSVD  
Keyword(4) Sparse Random Projection  
Keyword(5) Malimg Dataset  
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1st Author's Name Aziz Urahman Shafaq  
1st Author's Affiliation University of the Ryukyus (Univ. of the Ryukyus)
2nd Author's Name Takayuki Nakachi  
2nd Author's Affiliation University of the Ryukyus (Univ. of the Ryukyus)
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Speaker Author-1 
Date Time 2024-09-05 15:50:00 
Presentation Time 20 minutes 
Registration for EMM 
Paper # LOIS2024-25, IE2024-31, EMM2024-81 
Volume (vol) vol.124 
Number (no) no.175(LOIS), no.176(IE), no.177(EMM) 
Page pp.71-76 
#Pages 6 
Date of Issue 2024-08-28 (LOIS, IE, EMM) 

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