Towards Building Network Outlier Detection System for Network Traffic Monitoring

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0193358" target="_blank" >RIV/00216305:26230/26:0193358 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/63839172:_____/25:10133788

Výsledek na webu

<a href="http://dx.doi.org/10.1109/NOMS57970.2025.11073727" target="_blank" >http://dx.doi.org/10.1109/NOMS57970.2025.11073727</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NOMS57970.2025.11073727" target="_blank" >10.1109/NOMS57970.2025.11073727</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Building Network Outlier Detection System for Network Traffic Monitoring

Popis výsledku v původním jazyce

Traffic monitoring is important for supporting network security and management. Recent advancements have explored machine learning-based approaches to classify encrypted traffic, yet the challenge of obtaining current threat datasets persists, leaving supervised models reliant on outdated information. Outlier detection, which identifies anomalous network behavior without requiring labeled data, addresses this limitation by flagging suspicious deviations from expected patterns. This paper proposes a novel Network Outlier Detection System (NODS), a platform based on open-source software designed to detect outliers in network traffic by leveraging forecasting models. Our system was deployed and tested on a large ISP infrastructure. The evaluation of detected outliers over a one-month period showed key insights into system performance and provided valuable lessons for future deployment of outlier detection methods. This paper details the architecture of NODS, deployment, and performance while highlighting the challenges and lessons learned in building an effective outlier detection system for network traffic.

Název v anglickém jazyce

Towards Building Network Outlier Detection System for Network Traffic Monitoring

Popis výsledku anglicky

Traffic monitoring is important for supporting network security and management. Recent advancements have explored machine learning-based approaches to classify encrypted traffic, yet the challenge of obtaining current threat datasets persists, leaving supervised models reliant on outdated information. Outlier detection, which identifies anomalous network behavior without requiring labeled data, addresses this limitation by flagging suspicious deviations from expected patterns. This paper proposes a novel Network Outlier Detection System (NODS), a platform based on open-source software designed to detect outliers in network traffic by leveraging forecasting models. Our system was deployed and tested on a large ISP infrastructure. The evaluation of detected outliers over a one-month period showed key insights into system performance and provided valuable lessons for future deployment of outlier detection methods. This paper details the architecture of NODS, deployment, and performance while highlighting the challenges and lessons learned in building an effective outlier detection system for network traffic.

Druh

D - Stať ve sborníku

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

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

38th IEEE/IFIP Network Operations and Management Symposium (NOMS 2025)

ISBN

979-8-3315-3164-5

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

0-0

Název nakladatele

IEEE Communications Society

Místo vydání

Honolulu

Místo konání akce

Honolulu

Datum konání akce

12. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001556086900153