An encrypted network video stream dataset

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F23%3A43907123" target="_blank" >RIV/60076658:12310/23:43907123 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2352340923004535?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352340923004535?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dib.2023.109335" target="_blank" >10.1016/j.dib.2023.109335</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An encrypted network video stream dataset

Popis výsledku v původním jazyce

Most of the video content on the Internet today is distributed through online streaming platforms. To ensure user privacy, data transmissions are often encrypted using cryptographic protocols. In previous research, we first experimentally validated the idea that the amount of transmitted data belonging to a particular video stream is not constant over time or that it changes periodically and forms a specific fingerprint. Based on the knowledge of the fingerprint of a specific video stream, this video stream can be subsequently identified. Over several months of intensive work, our team has created a large dataset containing a large number of video streams that were captured by network traffic probes during their playback by end users. The video streams were deliberately chosen to fall thematically into pre-selected categories. We selected two primary platforms for streaming - PeerTube and YouTube The first platform was chosen because of the possibility of modifying any streaming parameters, while the second one was chosen because it is used by many people worldwide. Our dataset can be used to create and train machine learning models or heuristic algorithms, allowing encrypted video stream identification according to their content resp. type category or specifically.

Název v anglickém jazyce

An encrypted network video stream dataset

Popis výsledku anglicky

Most of the video content on the Internet today is distributed through online streaming platforms. To ensure user privacy, data transmissions are often encrypted using cryptographic protocols. In previous research, we first experimentally validated the idea that the amount of transmitted data belonging to a particular video stream is not constant over time or that it changes periodically and forms a specific fingerprint. Based on the knowledge of the fingerprint of a specific video stream, this video stream can be subsequently identified. Over several months of intensive work, our team has created a large dataset containing a large number of video streams that were captured by network traffic probes during their playback by end users. The video streams were deliberately chosen to fall thematically into pre-selected categories. We selected two primary platforms for streaming - PeerTube and YouTube The first platform was chosen because of the possibility of modifying any streaming parameters, while the second one was chosen because it is used by many people worldwide. Our dataset can be used to create and train machine learning models or heuristic algorithms, allowing encrypted video stream identification according to their content resp. type category or specifically.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Data in Brief

ISSN

2352-3409

e-ISSN

—

Svazek periodika

49

Číslo periodika v rámci svazku

AUG 2023

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

001027873400001

EID výsledku v databázi Scopus

2-s2.0-85163478728