Real-time communication (RTC) platforms have become increasingly popular in the last decade, together with the spread of broadband Internet access. They are nowadays a fundamental means for connecting people and supporting the economy, which relies more and more on forms of remote working. In this context, it is particularly important to act at the network level to ensure adequate Quality of Experience (QoE) to users, where proper traffic management policies are essential to prioritize RTC traffic. This, in turn, requires in-network devices to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify, in real-time, the media streams generated by RTC applications encapsulated in Secure Real Time Protocol (SRTP) flows. Using carefully tuned features extracted from packet characteristics, we train a model to classify streams into an ample set of classes, including media type (audio/video), video quality and redundant streams. To validate our approach, we use traffic from more than 88 hours of multi-party meeting calls made using the Cisco Webex Teams application. We reach an overall accuracy of 97% with a light-weight decision tree model, which makes decisions using only 1 second of traffic.

Online Classification of RTC Traffic / Perna, Gianluca; Markudova, Dena; Trevisan, Martino; Garza, Paolo; Meo, Michela; Munafò, Maurizio; Carofiglio, Giovanna. - ELETTRONICO. - (2021), pp. 1-6. ((Intervento presentato al convegno 2021 IEEE 18th Annual Consumer Communications & Networking Conference tenutosi a Las Vegas, NV, USA nel 9-12 Jan. 2021 [10.1109/CCNC49032.2021.9369470].

Online Classification of RTC Traffic

Perna, Gianluca;Markudova, Dena;Trevisan, Martino;Garza, Paolo;Meo, Michela;Munafò, Maurizio;Carofiglio, Giovanna
2021

Abstract

Real-time communication (RTC) platforms have become increasingly popular in the last decade, together with the spread of broadband Internet access. They are nowadays a fundamental means for connecting people and supporting the economy, which relies more and more on forms of remote working. In this context, it is particularly important to act at the network level to ensure adequate Quality of Experience (QoE) to users, where proper traffic management policies are essential to prioritize RTC traffic. This, in turn, requires in-network devices to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify, in real-time, the media streams generated by RTC applications encapsulated in Secure Real Time Protocol (SRTP) flows. Using carefully tuned features extracted from packet characteristics, we train a model to classify streams into an ample set of classes, including media type (audio/video), video quality and redundant streams. To validate our approach, we use traffic from more than 88 hours of multi-party meeting calls made using the Cisco Webex Teams application. We reach an overall accuracy of 97% with a light-weight decision tree model, which makes decisions using only 1 second of traffic.
978-1-7281-9794-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2874753