Rodyti trumpą aprašą

dc.contributor.authorGyamfi, Nana Kwame
dc.contributor.authorGoranin, Nikolaj
dc.contributor.authorČeponis, Dainius
dc.contributor.authorČenys, Antanas
dc.date.accessioned2023-09-18T16:28:27Z
dc.date.available2023-09-18T16:28:27Z
dc.date.issued2022
dc.identifier.issn2182-2069
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/114288
dc.description.abstractMalware detection is a quintessential task for every security for securing work stations, mobile devices, servers etc. This detection is mainly used for identifying malware that are causing malicious problems. The traditional detection system has a much lesser rate of detection rate and the chances of getting an error is higher as well. As the emerging technology revolutionized day by day, the usage of Deep Learning (DL) is highly influenced in these detection fields. So, this paper brings an effective DL based detection of malware in which the following are the stages: a) Data collection being carried from Malimg dataset, b) Pre-processing carried out to eliminate the unwanted noise from the dataset and passed to c) Feature extraction, where Principal Component Analysis (PCA) used for extracting required features, d) Feature selection where Particle Swarm Optimization (PSO) used for dimensionality reduction and finally passed for e) Classification where Convolutional Neural Network (CNN) used as a classifier for effective classification. These models are evaluated under measures like Accuracy, sensitivity, specificity, precision, recall, f1-score, TPR, FPR and detection rate over models like VGG16, VGG19, Densenet, Alexnent, Ensemble learning. The proposed system (D-WARE) gives much higher performance with a 96% accuracyeng
dc.formatPDF
dc.format.extentp. 102-115
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyDBLP
dc.relation.isreferencedbyDOAJ
dc.relation.isreferencedbyScopus
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://jisis.org/article/I4.007/69992/
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:152479558/datastreams/MAIN/content
dc.titleMalware detection using convolutional neural network, a deep learning framework: comparative analysis
dc.typeStraipsnis Scopus DB / Article in Scopus DB
dcterms.licenseCreative Commons – Attribution – NonCommercial – 4.0 International
dcterms.references36
dc.type.pubtypeS2 - Straipsnis Scopus DB / Scopus DB article
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyFundamentinių mokslų fakultetas / Faculty of Fundamental Sciences
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.studydirectionB04 - Informatikos inžinerija / Informatics engineering
dc.subject.studydirectionB02 - Informacijos sistemos / Information system
dc.subject.studydirectionB03 - Programų sistemos / Software engineering
dc.subject.vgtuprioritizedfieldsIK0101 - Informacijos ir informacinių technologijų sauga / Information and Information Technologies Security
dc.subject.ltspecializationsL106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies
dc.subject.enconvolutional neural network
dc.subject.endeep learning
dc.subject.enmalware
dc.subject.enparticle swarm optimization
dc.subject.enprincipal component analysis
dcterms.sourcetitleJournal of internet services and information security
dc.description.issueiss. 4
dc.description.volumevol. 12
dc.publisher.nameInnovative Information Science & Technology Research Group (ISYOU)
dc.identifier.doi10.58346/JISIS.2022.I4.007
dc.identifier.elaba152479558


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