Differentiating voice and data traffic using statistical properties

Buyukkayhan, Ahmet Salih; Kavak, Adnan; Yaprak, Ece

doi:10.1109/icecco.2013.6718232

Cited by 3 publications

(2 citation statements)

References 6 publications

(5 reference statements)

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“…In [39], UDP flows are classified into different classes, including Skype and RTP-based traffic,using SVM models and statistical signatures of the payload. The approach proposed in [40] leverages statistical properties of RTP to differentiate between voice and data traffic. The authors of [41] propose a method to detect WebRTC sessions at run-time based on statistical pattern recognition.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Classification of Real-Time Communications

Perna

Markudova

Trevisan

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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Real-time communication (RTC) applications have become largely popular in the last decade with the spread of broadband and mobile Internet access. Nowadays, these platforms are a fundamental means for connecting people and supporting businesses that increasingly rely on forms of remote work. In this context, it is of paramount importance to operate at the network level to ensure adequate Quality of Experience (QoE) for users, and appropriate traffic management policies are essential to prioritize RTC traffic. This in turn requires the network to be able to identify RTC streams and the type of content they carry.In this paper, we propose a machine learning-based application to classify media streams generated by RTC applications encapsulated in Secure Real-Time Protocol (SRTP) flows in real-time. Using carefully tuned features extracted from packet characteristics, we train models to classify streams into a variety of classes, including media type (audio/video), video quality, and redundant streams. We validate our approach using traffic from over 62 hours of multi-party meetings conducted using two popular RTC applications, namely Cisco Webex Teams and Jitsi Meet. We achieve an overall accuracy of 96% for Webex and 95% for Jitsi, using a lightweight decision tree model that makes decisions based solely on 1 second of real-time traffic. Our results show that models trained for a particular meeting software have difficulty when used with another one, although domain adaptation techniques facilitate the transfer of pretrained models.

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Section: Related Workmentioning

confidence: 99%

Real-Time Classification of Real-Time Communications

Perna

Markudova

Trevisan

et al. 2022

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

show abstract

“…In [15], UDP flows are classified with SVM models using statistical signatures of the payload, to various classes, including Skype and RTP-based traffic. The authors of [16] use statistical properties of RTP to differentiate between voice and data traffic. The authors of [17] propose a method to detect WebRTC sessions at run-time based on statistical pattern recognition.…”

Section: Related Workmentioning

confidence: 99%

Online Classification of RTC Traffic

Perna

Markudova

Trevisan

et al. 2021

2021 IEEE 18th Annual Consumer Communications &Amp; Networking Conference (CCNC)

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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.

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Real-time Application Identification of RTC Media Streams via Encrypted Traffic Analysis

Zhu

Cheng

et al. 2022

2022 International Conference on Computer Communications and Networks (ICCCN)

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Differentiating voice and data traffic using statistical properties

Cited by 3 publications

References 6 publications

Real-Time Classification of Real-Time Communications

Real-Time Classification of Real-Time Communications

Online Classification of RTC Traffic

Real-time Application Identification of RTC Media Streams via Encrypted Traffic Analysis

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