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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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Published on: November 26, 2019

A novel dataset for encrypted virtual private network traffic analysis.

Mohamed Naas¹, Jan Fesl^1,2

¹Department of Informatics, University of South Bohemia, Faculty of Science, Czechia.

|February 17, 2023

Summary

This summary is machine-generated.

This study introduces a new dataset for encrypted network traffic analysis. It aids data scientists in classifying encrypted virtual private networks (VPNs) and traffic patterns.

Keywords:

IP flow IPFIX Machine Learning Network traffic SSTP, OpenVPN Wireguard

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Area of Science:

Computer Science
Cybersecurity
Data Science

Background:

Network traffic encryption is crucial for anonymity and preventing data interception.
Encryption alone may not fully secure data transmissions, potentially exposing information.
Virtual private networks (VPNs) establish secure data tunnels for reliable transmission.

Purpose of the Study:

To construct and present a comprehensive dataset of generated encrypted network traffic.
To facilitate research in the classification of encrypted network traffic and VPNs.
To provide data for analyzing various network traffic types and VPN scenarios.

Main Methods:

Generated a large dataset of encrypted network traffic.
Included a general network traffic model with diverse traffic types (web, email, video conferencing, streaming, terminal services).
Collected data for traffic with different VPN types and without VPNs, including VPN connection handshakes.

Main Results:

The dataset encompasses varied network traffic models and scenarios.
It includes encrypted traffic data, VPN configurations, and initial connection handshakes.
The dataset is suitable for machine learning and data analysis tasks.

Conclusions:

The developed dataset serves as a valuable resource for data scientists.
It supports the advancement of encrypted network traffic and VPN classification techniques.
Further research can leverage this dataset to enhance network security analysis.