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An Analysis of Convolutional Neural Networks for detecting DGA

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00333206" target="_blank" >RIV/68407700:21230/18:00333206 - isvavai.cz</a>

  • Result on the web

    <a href="http://sedici.unlp.edu.ar/bitstream/handle/10915/73629/Documento_completo.pdf-PDFA.pdf?sequence=1&isAllowed=y" target="_blank" >http://sedici.unlp.edu.ar/bitstream/handle/10915/73629/Documento_completo.pdf-PDFA.pdf?sequence=1&isAllowed=y</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    An Analysis of Convolutional Neural Networks for detecting DGA

  • Original language description

    A Domain Generation Algorithm (DGA) is an algorithm togenerate domain names in a deterministic but seemly random way. Mal-ware use DGAs to generate the next domain to access the CommandControl (C&C) communication channel. Given the simplicity and veloc-ity associated to the domain generation process, machine learning detec-tion methods emerged as suitable detection solution. However, since theperiodical retraining becomes mandatory, a fast and accurate detectionmethod is needed. Convolutional neural network (CNN) are well knownfor performing real-time detection in fields like image and video recogni-tion. Therefore, they seem suitable for DGA detection. The present workis a preliminary analysis of the detection performance of CNN for DGAdetection. A CNN with a minimal architecture complexity was evaluatedon a dataset with 51 DGA malware families as well as normal domains.Despite its simple architecture, the resulting CNN model correctly de-tected more than 97% of total DGA domains with a false positive rateclose to 0.7%.

  • 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

    <a href="/en/project/TH02010990" target="_blank" >TH02010990: Ludus: Machine Learning and Game Theory to Collaboratively Defend Against Internet Threats</a><br>

  • Continuities

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

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

    Computer Science - CACIC 2018

  • ISBN

    978-950-658-472-6

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    1060-1069

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Tandil

  • Event date

    Oct 8, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article