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Adaptive network security through stream machine learning

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00325944" target="_blank" >RIV/68407700:21230/18:00325944 - isvavai.cz</a>

  • Result on the web

    <a href="https://dl.acm.org/citation.cfm?doid=3234200.3234246" target="_blank" >https://dl.acm.org/citation.cfm?doid=3234200.3234246</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3234200.3234246" target="_blank" >10.1145/3234200.3234246</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Adaptive network security through stream machine learning

  • Original language description

    Stream Machine Learning is rapidly gaining popularity within the network monitoring community as the big data produced by network devices and end-user terminals goes beyond the memory constraints of standard monitoring equipment. We consider a stream-based machine learning approach to network security, conceiving adaptive machine learning algorithms for the analysis of continuously evolving network data streams. Using a sliding-window adaptive-size approach, we show that adaptive random forests models are able to keep up with important concept drifts in the underlying network data streams, by keeping high accuracy with continuous re-training at concept drift detection times.

  • 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

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    SIGCOMM '18 Proceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos

  • ISBN

    978-1-4503-5915-3

  • ISSN

  • e-ISSN

  • Number of pages

    2

  • Pages from-to

    4-5

  • Publisher name

    ACM

  • Place of publication

    New York

  • Event location

    Budapest

  • Event date

    Aug 20, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article