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_____%2F25%3A10133770" target="_blank" >RIV/63839172:_____/25:10133770 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/25:00381365

Výsledek na webu

<a href="https://doi.org/10.1038/s41597-025-04603-x" target="_blank" >https://doi.org/10.1038/s41597-025-04603-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41597-025-04603-x" target="_blank" >10.1038/s41597-025-04603-x</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

Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. Most approaches to anomaly detection use methods based on forecasting. Extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing overestimation of anomaly detection algorithm performance and fabricating the illusion of progress. This manuscript tackles this issue by introducing a comprehensive dataset derived from 40 weeks of traffic transmitted by 275,000 active IP addresses in the CESNET3 network-an ISP network serving approximately half a million customers daily. It captures the behavior of diverse network entities, reflecting the variability typical of an ISP environment. This variability provides a realistic and challenging environment for developing forecasting and anomaly detection models, enabling evaluations that are closer to real-world deployment scenarios. It provides valuable insights into the practical deployment of forecast-based anomaly detection approaches.

Název v anglickém jazyce

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

Popis výsledku anglicky

Anomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. Most approaches to anomaly detection use methods based on forecasting. Extensive real-world network datasets for forecasting and anomaly detection techniques are missing, potentially causing overestimation of anomaly detection algorithm performance and fabricating the illusion of progress. This manuscript tackles this issue by introducing a comprehensive dataset derived from 40 weeks of traffic transmitted by 275,000 active IP addresses in the CESNET3 network-an ISP network serving approximately half a million customers daily. It captures the behavior of diverse network entities, reflecting the variability typical of an ISP environment. This variability provides a realistic and challenging environment for developing forecasting and anomaly detection models, enabling evaluations that are closer to real-world deployment scenarios. It provides valuable insights into the practical deployment of forecast-based anomaly detection approaches.

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

SCIENTIFIC DATA

ISSN

2052-4463

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

001435256600005

EID výsledku v databázi Scopus

2-s2.0-85219559884