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Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine Learning

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etc_noms24_postprint.pdf (613.0Kb)
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URI: https://hdl.handle.net/20.500.12761/1791
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Autor(es)
Akem, Aristide Tanyi-Jong; Fraysse, Guillaume; Fiore, Marco
Fecha
2024-05-05
Resumen
Encrypted 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.
Compartir
Ficheros
etc_noms24_postprint.pdf (613.0Kb)
Identificadores
URI: https://hdl.handle.net/20.500.12761/1791
Metadatos
Mostrar el registro completo del ítem

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