CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F24%3A10133861" target="_blank" >RIV/63839172:_____/24:10133861 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5281/zenodo.13382427" target="_blank" >http://dx.doi.org/10.5281/zenodo.13382427</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5281/zenodo.13382427" target="_blank" >10.5281/zenodo.13382427</a>

Jazyk výsledku

angličtina

Název v původním jazyce

CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting

Popis výsledku v původním jazyce

The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods.

Název v anglickém jazyce

CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting

Popis výsledku anglicky

The dataset called CESNET-TimeSeries24 was collected by long-term monitoring of selected statistical metrics for 40 weeks for each IP address on the ISP network CESNET3 (Czech Education and Science Network). The dataset encompasses network traffic from more than 275,000 active IP addresses, assigned to a wide variety of devices, including office computers, NATs, servers, WiFi routers, honeypots, and video-game consoles found in dormitories. Moreover, the dataset is also rich in network anomaly types since it contains all types of anomalies, ensuring a comprehensive evaluation of anomaly detection methods.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/VJ02010024" target="_blank" >VJ02010024: Analýza šifrovaného provozu pomocí síťových toků</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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