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ITC-net-audio-5: an audio streaming dataset for application identification in network traffic classification.

Mohammad Nikbakht¹, Mehdi Teimouri²

¹Information Theory and Coding (ITC) Laboratory, University of Tehran, Tehran, Iran.

BMC Research Notes

|February 27, 2024

Summary

Researchers created a new dataset of audio streaming network traffic from popular apps like Skype and WhatsApp. This resource aids in network traffic classification and application identification.

Keywords:

Application identification Audio Streaming Dataset Network Traffic classification Traffic capturing

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Area of Science:

Computer Science
Network Engineering
Data Science

Background:

Application identification is crucial for network traffic classification.
Existing datasets lack comprehensive audio streaming data.
There is a need for up-to-date resources for analyzing audio streaming applications.

Purpose of the Study:

To generate a novel dataset focused on audio streaming network traffic.
To support advancements in network traffic classification for audio applications.
To provide a valuable resource for researchers and practitioners in the field.

Main Methods:

Captured network traffic from five popular audio streaming applications: Google Meet, Skype, Telegram, WhatsApp, and SoundCloud.
Collected 500 PCAP files using Wireshark and PCAPdroid during voice calls and music playback.
Employed concurrent capture tools to minimize background traffic interference.

Main Results:

A comprehensive dataset of audio streaming network traffic was successfully generated.
The dataset includes diverse audio streaming scenarios from trending applications.
The data is suitable for training and evaluating network traffic classification models.

Conclusions:

The new dataset addresses the gap in publicly available audio streaming traffic data.
This resource will enhance the accuracy and efficiency of audio streaming application identification.
It serves as a foundation for future research in network traffic analysis and cybersecurity.