Deep Convolutional Neural Networks for DGA Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00331774" target="_blank" >RIV/68407700:21230/19:00331774 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/content/pdf/10.1007%2F978-3-030-20787-8_23.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007%2F978-3-030-20787-8_23.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-20787-8_23" target="_blank" >10.1007/978-3-030-20787-8_23</a>

Result language

angličtina

Original language name

Deep Convolutional Neural Networks for DGA Detection

Original language description

A Domain Generation Algorithm (DGA) is an algorithm to generate domain names in a deterministic but seemly random way. Malware use DGAs to generate the next domain to access the Command & Control (C&C) communication server. Given the simplicity of the generation process and speed at which the domains are generated, a fast and accurate detection method is required. Convolutional neural network (CNN) are well known for performing real-time detection in fields like image and video recognition. Therefore, they seemed suitable for DGA detection. The present work provides an analysis and comparison of the detection performance of a CNN for DGA detection. A CNN with a minimal architecture complexity was evaluated on a dataset with 51 DGA malware families and normal domains. Despite its simple architecture, the resulting CNN model correctly detected more than 97% of total DGA domains with a false positive rate close to 0.7%. 2019, Springer Nature Switzerland AG.

Czech name

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Czech description

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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)

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)

Publication year

2019

Confidentiality

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

Article name in the collection

Computer Science - CACIS 2018

ISBN

978-3-030-20786-1

ISSN

1865-0929

e-ISSN

—

Number of pages

14

Pages from-to

327-340

Publisher name

Springer VDI Verlag

Place of publication

Düsseldorf

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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