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Tytuł: Network Traffic Classification in an NFV Environment using Supervised ML Algorithms, Journal of Telecommunications and Information Technology, 2021, nr 3

Autor:

Latkoski, Pero ; Ilievski, Gjorgji

Data wydania:

2021, nr 3

Typ zasobu:

artykuł

Opis:

We have conducted research on the performance of six supervised machine learning (ML) algorithms used for network traffic classification in a virtual environment driven by network function virtualization (NFV). The performance-related analysis focused on the precision of the classification process, but also in time-intensity (speed) of the supervised ML algorithms. We devised specific traffic taxonomy using commonly used categories, with particular emphasis placed on VoIP and encrypted VoIP protocols serve as a basis of the 5G architecture. NFV is considered to be one of the foundations of 5G development, as the traditional networking components are fully virtualized, in many cases relaying on mixed cloud solutions, both of the premise- and public cloud-based variety. Virtual machines are being replaced by containers and application functions while most of the network traffic is flowing in the east-west direction within the cloud. The analysis performed has shown that in such an environment, the Decision Tree algorithm is best suited, among the six algorithms considered, for performing classification-related tasks, and offers the required speed that will introduce minimal delays in network flows, which is crucial in 5G networks, where packet delay requirements are of great significance. It has proven to be reliable and offered excellent overall performance across multiple network packet classes within a virtualized NFV network architecture. While performing the classification procedure, we were working only with the statistical network flow features, leaving out packet payload, source, destination- and port-related information, thus making the analysis valid not only from the technical, but also from the regulatory point of view

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Identyfikator zasobu:

ISSN 1509-4553, on-line: ISSN 1899-8852 ; oai:bc.itl.waw.pl:2193

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Temat i słowa kluczowe:

classification machine learning network traffic network functions virtualization

Kolekcje, do których przypisany jest obiekt:

Data ostatniej modyfikacji:

11 paź 2021

Data dodania obiektu:

11 paź 2021

Liczba wyświetleń treści obiektu:

48

Wszystkie dostępne wersje tego obiektu:

https://bc.itl.waw.pl/publication/2495

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Wyświetl opis w formacie OAI-PMH:

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Nazwa wydania Data
Ilievski, Gjorgji; Latkoski, Pero, Network Traffic Classification in an NFV Environment using Supervised ML Algorithms, Journal of Telecommunications and Information Technology, 2021, nr 3 11 paź 2021

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