Pipelined Decision Trees for Online Traffic Classification on FPGAs

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dc.authorwosidSOYLU, Tuncay/HHN-7426-2022
dc.authorwosiderdem, oğuzhan/AAG-6229-2019
dc.contributor.authorErdem, Oguzhan
dc.contributor.authorSoylu, Tuncay
dc.contributor.authorCarus, Aydin
dc.date.accessioned2024-06-12T11:16:33Z
dc.date.available2024-06-12T11:16:33Z
dc.date.issued2023
dc.departmentTrakya Üniversitesien_US
dc.description.abstractDecision tree (DT)-based machine learning (ML) algorithms are one of the preferred solutions for real-time internet traffic classification in terms of their easy implementation on hardware. However, the rapid increase in today's newly developed applications and the resulting diversity in internet traffic greatly increases the size of DTs. Therefore, the tree-based hardware classifiers cannot keep up with this growth in terms of resource usage and classification speed. To alleviate the problem, we propose to group application classes by certain rules and create an individual small DT per each group. In this article, a pipelined organization of multiple DT data structures, called pipelined decision trees, is proposed as a scalable solution to tree-based traffic classification. We also propose two distinct algorithms, namely confusion matrix-based class aggregation and leaf count-based class aggregation algorithms, to set group creation rules that allows traffic classification on pipelined smaller DTs in a hierarchical order. We further designed an hardware engine on field programmable gate arrays, which can search those pipelined trees within a single clock cycle by transforming them into bit vectors and implementing multiple range comparisons in parallel. Our architecture with 12 classes can run in 928.88 giga bit per second and achieve 96.04% accuracy.en_US
dc.identifier.doi10.1093/comjnl/bxad022
dc.identifier.issn0010-4620
dc.identifier.issn1460-2067
dc.identifier.scopus2-s2.0-85190774165en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1093/comjnl/bxad022
dc.identifier.urihttps://hdl.handle.net/20.500.14551/24365
dc.identifier.wosWOS:000976897900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherOxford Univ Pressen_US
dc.relation.ispartofComputer Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTraffic Classificationen_US
dc.subjectMachine Learningen_US
dc.subjectDecision-Treeen_US
dc.subjectData Structureen_US
dc.subjectFPGAen_US
dc.subjectFlow Associationen_US
dc.subjectNetworksen_US
dc.titlePipelined Decision Trees for Online Traffic Classification on FPGAsen_US
dc.typeArticleen_US

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