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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 |