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Malware classification by using deep learning framework

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dc.title Malware classification by using deep learning framework en
dc.contributor.author Toai, Tran Kim
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Hanh, Vo Thi Xuan
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-03-062323-4
dc.date.issued 2021
utb.relation.volume 1284
dc.citation.spage 84
dc.citation.epage 92
dc.event.title 5th International Conference on Green Technology and Sustainable Development, GTSD 2020
dc.event.location online
dc.event.sdate 2020-11-27
dc.event.edate 2020-11-28
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Science and Business Media Deutschland GmbH
dc.identifier.doi 10.1007/978-3-030-62324-1_8
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-030-62324-1_8
dc.subject classification en
dc.subject deep learning en
dc.subject machine learning en
dc.subject malware detection en
dc.subject random forest en
dc.subject SVM en
dc.description.abstract In this paper, we propose an original deep learning framework for malware classifying based on the malware behavior data. Currently, machine learning techniques are becoming popular for classifying malware. However, most of the existing machine learning methods for malware classifying use shallow learning algorithms such as Support Vector Machine, decision trees, Random Forest, and Naive Bayes. Recently, a deep learning approach has shown superior performance compared to traditional machine learning algorithms, especially in tasks such as image classification. In this paper we present the approach, in which malware binaries are converted to a grayscale image. Specifically, data in the raw form are converted into a 2D decimal valued matrix to represent an image. We propose here an original DNN architecture with deep denoising Autoencoder for feature compression, since the autoencoder is much more advantageous due to the ability to model complex nonlinear functions compared to principal component analysis (PCA) which is restricted to a linear map. The compressed malware features are then classified with a deep neural network. Preliminary test results are quite promising, with 96% classification accuracy on a malware database of 6000 samples with six different families of malware compared to SVM and Random Forest algorithms. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1010048
utb.identifier.obdid 43883336
utb.identifier.scopus 2-s2.0-85096609141
utb.source d-scopus
dc.date.accessioned 2020-12-09T01:52:46Z
dc.date.available 2020-12-09T01:52:46Z
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Tran Kim Toai 1,3, Roman Senkerik 2, Vo Thi Xuan Hanh 3, Ivan Zelinka 1 1 VSB-Technical University of Ostrava, 17, Listopadu 15/2172, 708 33 Ostrava-Poruba, Czech Republic {tran.kim.toai.st,ivan.zelinka}@vsb.cz 2 Faculty of Applied Informatics, Tom as Bata University in Zlin, T. G. Masaryka 5555, 760 01 Zlin, Czech Republic senkerik@utb.cz 3 Faculty of Economics, HCMC University of Technology and Education, No. 1, Vo van Ngan Street, Linh Chieu Ward, Ho Chi Minh, Vietnam {toaitk,hanhvtx}@hcmute.edu.vn
utb.fulltext.dates -
utb.scopus.affiliation VSB-Technical University of Ostrava, 17, Listopadu 15/2172, Ostrava-Poruba, 708 33, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, 760 01, Czech Republic; Faculty of Economics, HCMC University of Technology and Education, No. 1, Vo van Ngan Street, Linh Chieu Ward, Ho Chi Minh, Viet Nam
utb.fulltext.faculty Faculty of Applied Informatics
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