All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Active Learning Framework For Long-term Network Traffic Classification

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F23%3A10133626" target="_blank" >RIV/63839172:_____/23:10133626 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21240/23:00366203

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/10099065" target="_blank" >https://ieeexplore.ieee.org/document/10099065</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CCWC57344.2023.10099065" target="_blank" >10.1109/CCWC57344.2023.10099065</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Active Learning Framework For Long-term Network Traffic Classification

  • Original language description

    Recent network traffic classification methods benefit from machine learning (ML) technology. However, there are many challenges due to the use of ML, such as lack of high-quality annotated datasets, data drifts and other effects causing aging of datasets and ML models, high volumes of network traffic, etc. This paper presents the benefits of augmenting traditional workflows of ML training&amp;deployment and adaption of the Active Learning (AL) concept on network traffic analysis. The paper proposes a novel Active Learning Framework (ALF) to address this topic. ALF provides prepared software components that can be used to deploy an AL loop and maintain an ALF instance that continuously evolves a dataset and ML model automatically. Moreover, ALF includes monitoring, datasets quality evaluation, and optimization capabilities that enhance the current state of the art in the AL domain. The resulting solution is deployable for IP flow-based analysis of high-speed (100 Gb/s) networks, where it was evaluated for more than eight months. Additional use cases were evaluated on publicly available datasets.

  • Czech name

  • Czech description

Classification

  • 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)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

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

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2023 IEEE 13TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE, CCWC

  • ISBN

    979-8-3503-3286-5

  • ISSN

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    893-899

  • Publisher name

    IEEE

  • Place of publication

    NEW YORK

  • Event location

    Las Vegas, USA

  • Event date

    Mar 8, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000995182600138