ITC-net-audio-5: an audio streaming dataset for application identification in network traffic classification
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Updated: Jul 2, 2025

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Published on: November 26, 2019
Mohammad Nikbakht1, Mehdi Teimouri2
1Information Theory and Coding (ITC) Laboratory, University of Tehran, Tehran, Iran.
Objectives:
An essential aspect of network traffic classification is application identification. This involves capturing and analyzing the traffic patterns of applications. There are a few publicly available datasets that specifically capture streaming data from network-based applications. Therefore, our objective is to generate an up-to-date dataset with a focus on audio streaming data. This dataset can be a valuable resource for identifying audio streaming applications in the field of network traffic classification.
Data Description:
The dataset contains network traffic captured during audio streaming communications on five trending applications: Google Meet, Skype, Telegram, WhatsApp, and SoundCloud. It includes 500 files in PCAP format captured by Wireshark and PCAPdroid tools during voice calls and online music playback. The concurrent utilization of these tools facilitates the avoidance of capturing background traffic.
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