MFWDD: Model-based Feature Weight Drift Detection Showcased on TLS and QUIC Traffic

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F24%3A10133698" target="_blank" >RIV/63839172:_____/24:10133698 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/24:00377361

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10814630" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10814630</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/CNSM62983.2024.10814630" target="_blank" >10.23919/CNSM62983.2024.10814630</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MFWDD: Model-based Feature Weight Drift Detection Showcased on TLS and QUIC Traffic

Popis výsledku v původním jazyce

Machine learning (ML) represents an efficient and popular approach for network traffic classification. However, network traffic inspection is a challenging domain and trained models may degrade soon after deployment. Besides biases present during data captures and model creation, data drifts contribute significantly to ML model degradation. This paper proposes a novel method called Model-based Feature Weight Drift Detection (MFWDD) for concept drift detection. It is a part of a public software framework suited for dataset drift analysis tailored to the domain of network traffic. This work addresses TLS and QUIC service classification problems, examines a variety of experiments analyzing the evolution of the respective distributions, and observes their degradation over time on different ML features. The MFWDD framework guided TLS and QUIC services classification models retraining throughout an extensive period and not only prevented model degradation but also improved its performance and consistency over time.

Název v anglickém jazyce

MFWDD: Model-based Feature Weight Drift Detection Showcased on TLS and QUIC Traffic

Popis výsledku anglicky

Machine learning (ML) represents an efficient and popular approach for network traffic classification. However, network traffic inspection is a challenging domain and trained models may degrade soon after deployment. Besides biases present during data captures and model creation, data drifts contribute significantly to ML model degradation. This paper proposes a novel method called Model-based Feature Weight Drift Detection (MFWDD) for concept drift detection. It is a part of a public software framework suited for dataset drift analysis tailored to the domain of network traffic. This work addresses TLS and QUIC service classification problems, examines a variety of experiments analyzing the evolution of the respective distributions, and observes their degradation over time on different ML features. The MFWDD framework guided TLS and QUIC services classification models retraining throughout an extensive period and not only prevented model degradation but also improved its performance and consistency over time.

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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 20th International Conference on Network and Service Management (CNSM)

ISBN

978-3-903176-66-9

ISSN

2165-963X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Praha

Datum konání akce

28. 10. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001414325200095