A new platform for machine-learning-based network traffic classification

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dc.authoridBOZKIR, Ramazan/0000-0002-0032-4270en_US
dc.authoridCİCİOĞLU, MURTAZA/0000-0002-5657-7402en_US
dc.authoridÇalhan, Ali/0000-0002-5798-3103en_US
dc.authoridTogay, Cengiz/0000-0001-5739-1784en_US
dc.authorscopusid58107101900en_US
dc.authorscopusid57203170833en_US
dc.authorscopusid16548877100en_US
dc.authorscopusid15065979500en_US
dc.authorwosidBOZKIR, Ramazan/ITW-1816-2023en_US
dc.authorwosidCİCİOĞLU, MURTAZA/AAL-5004-2020en_US
dc.authorwosidÇalhan, Ali/H-1375-2014en_US
dc.contributor.authorBozkir, Ramazan
dc.contributor.authorCicioglu, Murtaza
dc.contributor.authorCalhan, Ali
dc.contributor.authorTogay, Cengiz
dc.date.accessioned2024-08-23T16:04:56Z
dc.date.available2024-08-23T16:04:56Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study provides a new platform for classifying encrypted network traffic based on machine learning (ML) techniques. The architecture of the platform is designed for real-world network traffic classification problems with performance-oriented, practical, and up-to-date software technologies. In addition, this study introduces a new feature extraction method to the literature. The proposed platform applies ML techniques with flowbased statistical features of encrypted network traffic and new feature extraction. It takes network traffic packets as input and passes them through feature extraction, data preparation, and ML stages. In the feature extraction stage, network flows are extracted from the network traffic data by calculating their features with the NFStream tool. During the data preparation stage, the dataset is transformed into a processable state for the ML algorithm with the Apache Spark framework. This stage also includes the feature selection operation. The ML stage runs GBTree, LightGBM, and XGBoost algorithms. Moreover, we use the MLflow framework in the proposed process management to observe the ML lifecycle, including experimentation, reproducibility, and deployment. The experimental results show that the XGBoost algorithm achieves the best result with an F1 score of above 99%.en_US
dc.identifier.doi10.1016/j.comcom.2023.05.010
dc.identifier.endpage14en_US
dc.identifier.issn0140-3664
dc.identifier.issn1873-703X
dc.identifier.scopus2-s2.0-85161281540en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.comcom.2023.05.010
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14403
dc.identifier.volume208en_US
dc.identifier.wosWOS:001015164300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputer Communicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNetwork traffic classificationen_US
dc.subjectMachine learningen_US
dc.subjectFeature extractionen_US
dc.subjectInterneten_US
dc.subjectEngineen_US
dc.subjectDeepen_US
dc.titleA new platform for machine-learning-based network traffic classificationen_US
dc.typeArticleen_US

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