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MalRed: An innovative approach for detecting malware using the red channel analysis of color images

Technological advancements have significantly progressed and innovated across various industries. However, these advancements have also led to an increase in cyberattacks using malware. Researchers have developed diverse techniques to detect and classify malware, including a visualization-based appr...

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Main Authors:	Shakir Hameed Shah S., Jamil N., ur Rehman Khan A., Mohd Sidek L., Alturki N., Muhammad Zain Z.
Other Authors:	59063194400
Format:	Article
Published:	Elsevier B.V. 2025
Subjects:	Classification (of information) Color Computer vision Digital forensics Discrete wavelet transforms Malware Textures Colour image De-noising Energy Gray-scale images Machine-learning Malwares Memory forensics Performance Red channels Wavelets transform Machine learning
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id	my.uniten.dspace-36506
record_format	dspace
spelling	my.uniten.dspace-365062025-03-03T15:42:46Z MalRed: An innovative approach for detecting malware using the red channel analysis of color images Shakir Hameed Shah S. Jamil N. ur Rehman Khan A. Mohd Sidek L. Alturki N. Muhammad Zain Z. 59063194400 36682671900 59065070300 58617132200 57226667238 59062250200 Classification (of information) Color Computer vision Digital forensics Discrete wavelet transforms Malware Textures Colour image De-noising Energy Gray-scale images Machine-learning Malwares Memory forensics Performance Red channels Wavelets transform Machine learning Technological advancements have significantly progressed and innovated across various industries. However, these advancements have also led to an increase in cyberattacks using malware. Researchers have developed diverse techniques to detect and classify malware, including a visualization-based approach that converts suspicious files into color or grayscale images, eliminating the need for domain-specific expertise. Nonetheless, converting files to color and grayscale images may result in the loss of texture details due to noise, adversely affecting the performance of machine learning models. The aim of this study is to present to assess the texture features and noise contributions of the red, green, and blue channels in color images. We propose a novel method to enhance model performance in terms of accuracy, precision, recall, f1-score, memory utilization, and computing cost during testing and training. This study introduces an approach involves separating color channels into individual red, green, and blue datasets and using various Discrete Wavelet Transform levels to reduce dimensions and remove noise. The extracted texture characteristics are then used as input for machine learning classifiers for training and prediction. Through comprehensive evaluation, we compare the performance of grayscale images with that of the red, green, and blue datasets. The results show that grayscale images significantly lose critical textural artifacts and perform worse than the color channels. Notably, employing extra tree classifiers yielded the best results, achieving an accuracy of 98.37%, precision of 98.64%, recall of 97.60%, and an f1-score of 98.04% with the red channel of color dataset. ? 2024 Final 2025-03-03T07:42:46Z 2025-03-03T07:42:46Z 2024 Article 10.1016/j.eij.2024.100478 2-s2.0-85192306529 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192306529&doi=10.1016%2fj.eij.2024.100478&partnerID=40&md5=0ffaa11781a784d9c9fc683a7db6e13a https://irepository.uniten.edu.my/handle/123456789/36506 26 100478 All Open Access; Gold Open Access Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Classification (of information) Color Computer vision Digital forensics Discrete wavelet transforms Malware Textures Colour image De-noising Energy Gray-scale images Machine-learning Malwares Memory forensics Performance Red channels Wavelets transform Machine learning
spellingShingle	Classification (of information) Color Computer vision Digital forensics Discrete wavelet transforms Malware Textures Colour image De-noising Energy Gray-scale images Machine-learning Malwares Memory forensics Performance Red channels Wavelets transform Machine learning Shakir Hameed Shah S. Jamil N. ur Rehman Khan A. Mohd Sidek L. Alturki N. Muhammad Zain Z. MalRed: An innovative approach for detecting malware using the red channel analysis of color images
description	Technological advancements have significantly progressed and innovated across various industries. However, these advancements have also led to an increase in cyberattacks using malware. Researchers have developed diverse techniques to detect and classify malware, including a visualization-based approach that converts suspicious files into color or grayscale images, eliminating the need for domain-specific expertise. Nonetheless, converting files to color and grayscale images may result in the loss of texture details due to noise, adversely affecting the performance of machine learning models. The aim of this study is to present to assess the texture features and noise contributions of the red, green, and blue channels in color images. We propose a novel method to enhance model performance in terms of accuracy, precision, recall, f1-score, memory utilization, and computing cost during testing and training. This study introduces an approach involves separating color channels into individual red, green, and blue datasets and using various Discrete Wavelet Transform levels to reduce dimensions and remove noise. The extracted texture characteristics are then used as input for machine learning classifiers for training and prediction. Through comprehensive evaluation, we compare the performance of grayscale images with that of the red, green, and blue datasets. The results show that grayscale images significantly lose critical textural artifacts and perform worse than the color channels. Notably, employing extra tree classifiers yielded the best results, achieving an accuracy of 98.37%, precision of 98.64%, recall of 97.60%, and an f1-score of 98.04% with the red channel of color dataset. ? 2024
author2	59063194400
author_facet	59063194400 Shakir Hameed Shah S. Jamil N. ur Rehman Khan A. Mohd Sidek L. Alturki N. Muhammad Zain Z.
format	Article
author	Shakir Hameed Shah S. Jamil N. ur Rehman Khan A. Mohd Sidek L. Alturki N. Muhammad Zain Z.
author_sort	Shakir Hameed Shah S.
title	MalRed: An innovative approach for detecting malware using the red channel analysis of color images
title_short	MalRed: An innovative approach for detecting malware using the red channel analysis of color images
title_full	MalRed: An innovative approach for detecting malware using the red channel analysis of color images
title_fullStr	MalRed: An innovative approach for detecting malware using the red channel analysis of color images
title_full_unstemmed	MalRed: An innovative approach for detecting malware using the red channel analysis of color images
title_sort	malred: an innovative approach for detecting malware using the red channel analysis of color images
publisher	Elsevier B.V.
publishDate	2025
_version_	1825816274753552384
score	13.244413

MalRed: An innovative approach for detecting malware using the red channel analysis of color images

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