Analysis of Statistical Distribution Changes of Input Features in Network Traffic Classification Domain

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F24%3A10133690" target="_blank" >RIV/63839172:_____/24:10133690 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/24:00375887

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10575630" target="_blank" >https://ieeexplore.ieee.org/document/10575630</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NOMS59830.2024.10575630" target="_blank" >10.1109/NOMS59830.2024.10575630</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of Statistical Distribution Changes of Input Features in Network Traffic Classification Domain

Popis výsledku v původním jazyce

This study investigates the evolving landscape of network traffic monitoring, which is crucial for maintaining computer network services and security. Traditional methods like Deep Packet Inspection (DPI) face challenges due to increased privacy protection through encryption, prompting a shift towards statistical-based detection using Machine Learning (ML). On the other hand, ML struggles with long-term evaluation due to various distribution changes. This study focuses on the CESNET-TLS-Year22 dataset, derived from one year of TLS network traffic on the CESNET2 backbone. Described research explores the behavior of modern protocols in real-world scenarios and their impact on dataset quality. The main result of our analysis is the identification of the Weekend phenomenon in network traffic classification that is generally overlooked during ML model training.

Název v anglickém jazyce

Analysis of Statistical Distribution Changes of Input Features in Network Traffic Classification Domain

Popis výsledku anglicky

This study investigates the evolving landscape of network traffic monitoring, which is crucial for maintaining computer network services and security. Traditional methods like Deep Packet Inspection (DPI) face challenges due to increased privacy protection through encryption, prompting a shift towards statistical-based detection using Machine Learning (ML). On the other hand, ML struggles with long-term evaluation due to various distribution changes. This study focuses on the CESNET-TLS-Year22 dataset, derived from one year of TLS network traffic on the CESNET2 backbone. Described research explores the behavior of modern protocols in real-world scenarios and their impact on dataset quality. The main result of our analysis is the identification of the Weekend phenomenon in network traffic classification that is generally overlooked during ML model training.

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

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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

NOMS 2024-2024 IEEE Network Operations and Management Symposium

ISBN

979-8-3503-2793-9

ISSN

2374-9709

e-ISSN

—

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Seoul, South Korea

Datum konání akce

6. 5. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001270140300155