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

Result code in 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>

Alternative codes found

RIV/68407700:21240/24:00377361

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/VJ02010024" target="_blank" >VJ02010024: Flow-based Encrypted Traffic Analysis</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Article name in the collection

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

ISBN

978-3-903176-66-9

ISSN

2165-963X

e-ISSN

—

Number of pages

5

Pages from-to

—

Publisher name

IEEE

Place of publication

New York

Event location

Praha

Event date

Oct 28, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001414325200095