Multichannel Histograms for Flow Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0193582" target="_blank" >RIV/00216305:26230/26:0193582 - isvavai.cz</a>

Alternative codes found

RIV/63839172:_____/25:10133791

Result on the web

<a href="http://dx.doi.org/10.1109/NOMS57970.2025.11073699" target="_blank" >http://dx.doi.org/10.1109/NOMS57970.2025.11073699</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NOMS57970.2025.11073699" target="_blank" >10.1109/NOMS57970.2025.11073699</a>

Result language

angličtina

Original language name

Multichannel Histograms for Flow Classification

Original language description

Encrypted traffic poses increasing challenges for effective network monitoring and management. To address this, machine learning and deep learning techniques are commonly applied to encrypted traffic classification by analyzing the statistical properties of network flows, such as packet lengths and inter-arrival times. Rather than relying on point estimates of these properties, we aim to obtain a comprehensive view of the distribution using histograms. In this paper, we propose and analyze a multichannel 2D histogram representation that incorporates packet lengths, directions, and inter-arrival times, extending the prior well-performing flowpic representation. We classify these histograms using a 2D convolutional neural network with residual connections, improving classification accuracy over previous methods.

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

2025

Confidentiality

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

Article name in the collection

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

ISBN

9798331531638

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

0-0

Publisher name

IEEE Communications Society

Place of publication

Honolulu

Event location

Honolulu

Event date

May 12, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001556086900125