Dataset: Advanced Similarity Metrics for IP Flow Data Analytics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F24%3A10133857" target="_blank" >RIV/63839172:_____/24:10133857 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.5281/zenodo.14035306" target="_blank" >http://dx.doi.org/10.5281/zenodo.14035306</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5281/zenodo.14035306" target="_blank" >10.5281/zenodo.14035306</a>

Result language

angličtina

Original language name

Dataset: Advanced Similarity Metrics for IP Flow Data Analytics

Original language description

Analysis of encrypted traffic in computer networks is intricate due to reduced visibility in transmitted content. Machine-learning techniques applied to data representing characteristics of traffic flows provide powerful tools for network monitoring or intrusion detection. Since real-world datasets are scarce, we present a novel traffic classification dataset with TLS traffic. The dataset contains three days (19-08-2022 -- 21-08-2022) of anonymized communication on CESNET3 ISP network, which is used by approximately half a million users daily.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/VJ02010024" target="_blank" >VJ02010024: Flow-based Encrypted Traffic Analysis</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

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