Classified VPN network traffic flow using time related to artificial neural network

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dc.contributor.authorMohamed, Saad Abdalla Agaili
dc.contributor.authorKurnaz, Sefer
dc.date.accessioned2024-08-15T12:18:01Z
dc.date.available2024-08-15T12:18:01Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractVPNs are vital for safeguarding communication routes in the continually changing cybersecurity world. However, increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks (ANN). This paper aims to provide a reliable system that can identify a virtual private network (VPN) traffic fromintrusion attempts, data exfiltration, and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns. Next, we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions. To effectively process and categorize encrypted packets, the neural network model has input, hidden, and output layers. We use advanced feature extraction approaches to improve theANN's classification accuracy by leveraging network traffic's statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance. The suggested ANN-based categorization method is extensively tested and analyzed. Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision, recall, and F1-score with 98.79% accuracy. This study improves VPN security and protects against new cyberthreats. Classifying VPNtraffic flows effectively helps enterprises protect sensitive data, maintain network integrity, and respond quickly to security problems. This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.en_US
dc.identifier.citationMohamed, S. A. A., Kurnaz, S. (2024). Classified VPN network traffic flow using time related to artificial neural network. Computers, Materials and Continua, 80(1), 819-841. 10.32604/cmc.2024.050474en_US
dc.identifier.endpage841en_US
dc.identifier.issn1546-2218
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85200460253
dc.identifier.scopusqualityQ1
dc.identifier.startpage819en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4807
dc.identifier.volume80en_US
dc.identifier.wosWOS:001290839400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorMohamed, Saad Abdalla Agaili
dc.institutionauthorKurnaz, Sefer
dc.language.isoen
dc.publisherTech Science Pressen_US
dc.relation.ispartofComputers, Materials and Continua
dc.relation.isversionof10.32604/cmc.2024.050474en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectANNen_US
dc.subjectClassificationen_US
dc.subjectData exfiltrationen_US
dc.subjectEncrypted trafficen_US
dc.subjectFeature extractionen_US
dc.subjectIntrusion detectionen_US
dc.subjectNetwork Securityen_US
dc.subjectNetwork traffic flowen_US
dc.subjectVPNen_US
dc.titleClassified VPN network traffic flow using time related to artificial neural network
dc.typeArticle

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