Binary and Multi-Class Malware Threads Classification
The security of a computer system can be harmed by specific applications, such as malware. Malware comprises unwanted, dangerous enemies that aim to compromise the security and generate significant loss. Consequently, Malware Detection (MD) and Malware Classification (MC) has emerged as a key issue...
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my.uniten.dspace-266262023-05-29T17:35:57Z Binary and Multi-Class Malware Threads Classification Ahmed I.T. Jamil N. Din M.M. Hammad B.T. 57193324906 36682671900 58032385600 57193327622 The security of a computer system can be harmed by specific applications, such as malware. Malware comprises unwanted, dangerous enemies that aim to compromise the security and generate significant loss. Consequently, Malware Detection (MD) and Malware Classification (MC) has emerged as a key issue for the cybersecurity society. MD only involves locating malware without determining what kind of malware it is, but MC comprises assigning a class of malware to a particular sample. Recently, a few techniques for analyzing malware quickly have been put out. However, there remain numerous difficulties, such as the low classification accuracy of samples from related malware families, the computational complexity, and consumption of resources. These difficulties make detecting and classifying malware very challenging. Therefore, in this paper, we proposed an efficient malware detection and classification technique that combines Segmentation-based Fractal Texture Analysis (SFTA) and Gaussian Discriminant Analysis (GDA). The outcomes of the experiment demonstrate that the SFTA-GDA produces a high classification rate. There are three main steps involved in our malware analysis, namely: (i) malware conversion; (ii) feature extraction; and (iii) classification. We initially convert the RGB malware images into grayscale malware images for effective malware analysis. The SFTA and Gabor features are then extracted from gray-scale images in the feature extraction step. Finally, the classification is carried out by GDA and Naive Bayes (NB). The proposed method is evaluated on a common MaleVis dataset. The proposed SFTA-GDA is the effective choice since it produces the highest accuracy rate across all families of the MaleVis Database. Experimental findings indicate that the accuracy rate was 98%, which is higher than the overall accuracy from the existing state-of-the-art methods. � 2022 by the authors. Final 2023-05-29T09:35:57Z 2023-05-29T09:35:57Z 2022 Article 10.3390/app122412528 2-s2.0-85144898308 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144898308&doi=10.3390%2fapp122412528&partnerID=40&md5=29f95de0e0e7e0f519c143f9910b0ca5 https://irepository.uniten.edu.my/handle/123456789/26626 12 24 12528 All Open Access, Gold MDPI Scopus |
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The security of a computer system can be harmed by specific applications, such as malware. Malware comprises unwanted, dangerous enemies that aim to compromise the security and generate significant loss. Consequently, Malware Detection (MD) and Malware Classification (MC) has emerged as a key issue for the cybersecurity society. MD only involves locating malware without determining what kind of malware it is, but MC comprises assigning a class of malware to a particular sample. Recently, a few techniques for analyzing malware quickly have been put out. However, there remain numerous difficulties, such as the low classification accuracy of samples from related malware families, the computational complexity, and consumption of resources. These difficulties make detecting and classifying malware very challenging. Therefore, in this paper, we proposed an efficient malware detection and classification technique that combines Segmentation-based Fractal Texture Analysis (SFTA) and Gaussian Discriminant Analysis (GDA). The outcomes of the experiment demonstrate that the SFTA-GDA produces a high classification rate. There are three main steps involved in our malware analysis, namely: (i) malware conversion; (ii) feature extraction; and (iii) classification. We initially convert the RGB malware images into grayscale malware images for effective malware analysis. The SFTA and Gabor features are then extracted from gray-scale images in the feature extraction step. Finally, the classification is carried out by GDA and Naive Bayes (NB). The proposed method is evaluated on a common MaleVis dataset. The proposed SFTA-GDA is the effective choice since it produces the highest accuracy rate across all families of the MaleVis Database. Experimental findings indicate that the accuracy rate was 98%, which is higher than the overall accuracy from the existing state-of-the-art methods. � 2022 by the authors. |
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57193324906 |
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57193324906 Ahmed I.T. Jamil N. Din M.M. Hammad B.T. |
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Ahmed I.T. Jamil N. Din M.M. Hammad B.T. |
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Ahmed I.T. Jamil N. Din M.M. Hammad B.T. Binary and Multi-Class Malware Threads Classification |
author_sort |
Ahmed I.T. |
title |
Binary and Multi-Class Malware Threads Classification |
title_short |
Binary and Multi-Class Malware Threads Classification |
title_full |
Binary and Multi-Class Malware Threads Classification |
title_fullStr |
Binary and Multi-Class Malware Threads Classification |
title_full_unstemmed |
Binary and Multi-Class Malware Threads Classification |
title_sort |
binary and multi-class malware threads classification |
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MDPI |
publishDate |
2023 |
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1806428100597121024 |
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13.18916 |