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
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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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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
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e-ISSN
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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
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