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

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dc.citedby1
dc.contributor.authorShakir Hameed Shah S.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorur Rehman Khan A.en_US
dc.contributor.authorMohd Sidek L.en_US
dc.contributor.authorAlturki N.en_US
dc.contributor.authorMuhammad Zain Z.en_US
dc.contributor.authorid59063194400en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid59065070300en_US
dc.contributor.authorid58617132200en_US
dc.contributor.authorid57226667238en_US
dc.contributor.authorid59062250200en_US
dc.date.accessioned2025-03-03T07:42:46Z
dc.date.available2025-03-03T07:42:46Z
dc.date.issued2024
dc.description.abstractTechnological 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. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo100478
dc.identifier.doi10.1016/j.eij.2024.100478
dc.identifier.scopus2-s2.0-85192306529
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85192306529&doi=10.1016%2fj.eij.2024.100478&partnerID=40&md5=0ffaa11781a784d9c9fc683a7db6e13a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36506
dc.identifier.volume26
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleEgyptian Informatics Journal
dc.subjectClassification (of information)
dc.subjectColor
dc.subjectComputer vision
dc.subjectDigital forensics
dc.subjectDiscrete wavelet transforms
dc.subjectMalware
dc.subjectTextures
dc.subjectColour image
dc.subjectDe-noising
dc.subjectEnergy
dc.subjectGray-scale images
dc.subjectMachine-learning
dc.subjectMalwares
dc.subjectMemory forensics
dc.subjectPerformance
dc.subjectRed channels
dc.subjectWavelets transform
dc.subjectMachine learning
dc.titleMalRed: An innovative approach for detecting malware using the red channel analysis of color imagesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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