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dc.contributor.authorAkem, Aristide Tanyi-Jong 
dc.contributor.authorFraysse, Guillaume
dc.contributor.authorFiore, Marco 
dc.date.accessioned2024-02-06T10:24:18Z
dc.date.available2024-02-06T10:24:18Z
dc.date.issued2024-05-05
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1791
dc.description.abstractEncrypted Traffic Classification (ETC) has become an important area of research with Machine Learning (ML) methods being the state-of-the-art. However, most existing solutions either rely on offline ETC based on collected network data or on online ETC with models running in the control plane of Software-Defined Networks (SDN), all of which do not run at line rate and would not meet latency requirements of time-sensitive applications in modern networks. This work leverages recent advances in data plane programmability to achieve real-time ETC in programmable switches at line rate, with high throughput and low latency. The proposed solution comprises (i) an ETC-aware Random Forest (RF) modelling process where only features based on packet size and packet arrival times are used, and (ii) an encoding of the trained RF model into production-grade P4-programmable switches. The performance of the proposed in-switch ETC framework is evaluated using 3 encrypted traffic datasets with experiments in a real-world testbed with Intel Tofino switches, in the presence of background traffic at 40 Gbps. Results show how the solution achieves high classification accuracy of up to 95%, with sub-microsecond delay, while consuming on average less than 10% of total available switch hardware resources.es
dc.description.sponsorshipSmart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101139270es
dc.description.sponsorshipEuropean Union’s Horizon Europe research and innovation programme under Marie Skłodowska-Curie grant agreement no. 860239es
dc.language.isoenges
dc.titleEncrypted Traffic Classification at Line Rate in Programmable Switches with Machine Learninges
dc.typeconference objectes
dc.conference.date6-10 May 2024es
dc.conference.placeSeoul, South Koreaes
dc.conference.titleIEEE/IFIP Network Operations and Management Symposium*
dc.event.typeconferencees
dc.pres.typepaperes
dc.type.hasVersionAMes
dc.rights.accessRightsopen accesses
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/860239es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/HORIZON-JU-SNS-2023/101139270es
dc.relation.projectNameBANYAN (Big dAta aNalYtics for radio Access Networks)es
dc.relation.projectNameORIGAMI (Optimized resource integration and global architecture for mobile infrastructure for 6G)es
dc.subject.keywordEncrypted traffic classificationes
dc.subject.keywordmachine learninges
dc.subject.keywordprogrammable switches
dc.subject.keywordP4es
dc.subject.keywordrandom forestes
dc.description.refereedTRUEes
dc.description.statusinpresses


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