Publication:
A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers

Simple item page
dc.citedby3
dc.contributor.authorAhmed I.T.en_US
dc.contributor.authorHammad B.T.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorid57193324906en_US
dc.contributor.authorid57193327622en_US
dc.contributor.authorid36682671900en_US
dc.date.accessioned2025-03-03T07:47:31Z
dc.date.available2025-03-03T07:47:31Z
dc.date.issued2024
dc.description.abstractThree frequent factors such as low classification accuracy, computational complexity, and resource consumption have an impact on malware evaluation methods. These challenges are exacerbated by elements such as unbalanced data environments and specific feature generation. To address these challenges, we aim to identify optimal texture features and classifiers for effective malware detection. The article outlines a method that consists of four stages: malware conversion to grayscale, feature extraction using (segmentation-based fractal texture analysis (SFTA), Local Binary Pattern (LBP), Haralick, Gabor, and Tamura), classification using (Gaussian Discriminant Analysis (GDA), k-Nearest Neighbor (KNN), Logistic, Support Vector Machines (SVM), Random Forest (RF), Extreme Learning Machine (Ensemble)), and finally the evaluation. Using the Malimg imbalanced and MaleVis balanced datasets, we assess classifier performance and feature effectiveness. Comparative analysis indicates that KNN outperforms other classifiers in terms of Accuracy, Error, F1, and Precision, while SVM and RF as runners-up. Gabor performs better in MaleVis, whereas the SFTA feature performs better under the Malimg dataset. The proposed SFTA-KNN and Gabor-KNN methods achieve 96.29% and 98.02% accuracy, respectively, surpassing current state-of-the-art approaches. Additionally, higher computing performance is achieved by using fewer dimensions when employing our feature extraction method. ? 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2024.3354959
dc.identifier.epage11519
dc.identifier.scopus2-s2.0-85182920061
dc.identifier.spage11500
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85182920061&doi=10.1109%2fACCESS.2024.3354959&partnerID=40&md5=8b3cad6dccf703286e6ebddfcf58c8d1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37104
dc.identifier.volume12
dc.pagecount19
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectDiscriminant analysis
dc.subjectExtraction
dc.subjectImage retrieval
dc.subjectLocal binary pattern
dc.subjectMalware
dc.subjectNearest neighbor search
dc.subjectStatistical tests
dc.subjectSupport vector machines
dc.subjectClassification-tree analysis
dc.subjectFeatures extraction
dc.subjectGabor
dc.subjectGabor-k-near neighbor
dc.subjectGaussian discriminant analyse
dc.subjectGaussians
dc.subjectLocal binary patterns
dc.subjectMalevi dataset
dc.subjectMalimg
dc.subjectMalware detection
dc.subjectMalwares
dc.subjectSegmentation-based fractal texture analyse
dc.subjectSegmentation-based fractal texture analyse-k-near neighbor
dc.subjectSupport vectors machine
dc.subjectTamura
dc.subjectTexture analysis
dc.subjectFeature extraction
dc.titleA Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiersen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS