Fast RTP Detection and Codecs Classification in Internet Traffic

Matoušek, Petr; Ryšavý, Ondřej; Kmeť, Martin

doi:10.15394/jdfsl.2014.1174

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…Our codec identifier method is based on the Codec Mapper Table, similar to the approaches used by Li, Ma, and Jin and Matoušek, Ryšavý, and Kmet . Both approaches used the timestamp difference of RTP packets as the key feature to map the codecs.…”

Section: Methodsmentioning

confidence: 99%

“…In a recent study, Matoušek, Ryšavý, and Kmet presented an approach to detect real‐time transfer protocol (RTP) stream by analyzing individual packet headers and the flow of the UDP packets stream over the network. The authors also proposed a codec identification method, based on the calculation of timeframe difference in each RTP packet .…”

Section: Related Workmentioning

confidence: 99%

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Can Android VoIP voice conversations be decoded? I can eavesdrop on your Android VoIP communication

Pokharel

Choo

Liu

2016

Concurrency and Computation

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Summary Voice over Internet Protocol (VoIP) is increasingly used by both individuals and corporate users. Recent studies had suggested that communications using VoIP apps may not be encrypted, although it has not been demonstrated that original conversations could be recovered from unencrypted communications that were subsequently intercepted. In this research, we propose a process for codec identification and decoding of captured communications from 15 popular Android VoIP apps. We are able to recover the original voice conversations from intercepted app‐to‐app voice calls of nine apps and app‐to‐phone calls from 10 apps. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Can Android VoIP voice conversations be decoded? I can eavesdrop on your Android VoIP communication

Pokharel

Choo

Liu

2016

Concurrency and Computation

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show abstract

“…The article [9] presents a fast multi-stage method for online detection of RTP streams and the codec format of transmitted voice or video traffic. This method includes an RTP detector to filter packets based on specific values from UDP and RTP headers.…”

Section: Related Workmentioning

confidence: 99%

QoS Assessment for Video and Audio Data based on a Testbed for Multimedia Wireless ad Hoc Networks

Anh

Giang

2022

J. Phys.: Conf. Ser.

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Currently, with the fast modernization of wireless networking technologies as well as the very complex needs of the network users, multimedia data takes for a large proportion in the types of network services, this type of data requires Quality of Service (QoS) differs from traditional data, by ensuring the listening and viewing elements of the user. In this paper, we analyse the effect of video and voice data on QoS of wireless network services based on the establishment of an experimental evaluation (emulation) system or testbed, thereby providing empirical analysis on the effect of multimedia data on mobile wireless QoS.

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“…Authors of [50] train machine learning classifiers to distinguish, among other classes, video and audio flows, targeting the WeChat messaging application. The approach presented in [51] identifies 20 codecs used for compression of audio,based on packet size, RTP timestamp delta, payload type and ratio between RTP timestamp delta and payload size. However, they do not use machine learning but a simple lookup table.…”

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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Fast RTP Detection and Codecs Classification in Internet Traffic

Cited by 6 publications

References 6 publications

Can Android VoIP voice conversations be decoded? I can eavesdrop on your Android VoIP communication

Can Android VoIP voice conversations be decoded? I can eavesdrop on your Android VoIP communication

QoS Assessment for Video and Audio Data based on a Testbed for Multimedia Wireless ad Hoc Networks

Real-Time Classification of Real-Time Communications

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