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A Better Infected Hosts Detection Combining Ensemble Learning and Threat Intelligence

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00348181" target="_blank" >RIV/68407700:21230/19:00348181 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-030-48325-8_23" target="_blank" >https://doi.org/10.1007/978-3-030-48325-8_23</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-48325-8_23" target="_blank" >10.1007/978-3-030-48325-8_23</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    A Better Infected Hosts Detection Combining Ensemble Learning and Threat Intelligence

  • Original language description

    Ensemble learning techniques have been successfully proposed and used to improve threats detection in cybersecurity. These techniques usually improve the detection results by combining algorithms that together have less errors. However there has not been any ensemble learning algorithm used to classify network flows when several methods are used to give individual detections for each of the flows. The state of the art in the use of ensemble learning techniques was analyzed to find an alternative for the current intrusion detection mechanisms. This research proposes to incorporate ensemble learning to the Stratosphere Linux IPS (SLIPS), a behavioral-based intrusion detection and prevention system that uses machine learning algorithms to detect malicious behaviors. Our ensembling method is used to obtain better results, taking advantage of the benefits of SLIPS' classifiers and modules. A contribution of our method is to extend the ensembling techniques by considering Threat Intelligence blacklists feeds as part of the detections. We present the results of the first stage of this project, i.e. ensemble learning algorithms to classify individual flows when they have multiple labels. on the other hand we also present the results corresponding to the second stage of our project, i.e. the detection of groups of flows going to the same destination IP.

  • 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

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    CACIC 2019: 25th Argentine Congress of Computer Science

  • ISBN

    978-3-030-48324-1

  • ISSN

    1865-0929

  • e-ISSN

  • Number of pages

    12

  • Pages from-to

    354-365

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Rio Cuarto

  • Event date

    Oct 14, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article