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The repository that contains the algorithms for generating domain names, dictionaries of malicious domain names. Developed to research the possibility of applying machine learning and neural networks to detect and classify malicious domains.

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DGA

The repository that contains the algorithms for generating domain names, dictionaries of malicious domain names. Developed to research the possibility of applying machine learning and neural networks to detect and classify malicious domains. List of wordlist's

#####alexa.csv

alexa top million

#####opendns-top-domains.txt

a few dns domain's from opendns

#####zeus.txt

domain's from GameoverZeus.py http://garage4hackers.com/entry.php?b=3081

#####cryptolocker.txt

domain's from Сryptolocker.pl

#####pushdo.txt

domain's from PushDo.py http://www.garage4hackers.com/entry.php?b=3080

#####rovnix.txt

https://www.csis.dk/en/csis/news/4472/
http://www.constitution.org/usdeclar.txt

#####conficker.txt

domain's from Conficker.c

#####tinba.txt

domain's from Tinba.py http://garage4hackers.com/entry.php?b=3086

#####matsnu.txt

domain's from Matsnu.py http://www.seculert.com/blog/2014/11/dgas-a-domain-generation-evolution.html

#####ramdo.txt

domain's from Ramdo.cpp

#####the translation from id to name

0 - legit
1 - cryptolocker
2 - zeus
3 - pushdo
4 - rovnix
5 - tinba
6 - conficker
7 - matsnu
8 - ramdo

About

Author: Andrey Abakumov ( andrewaeva@ya.ru )

License: GNU General Public License v2 (http://opensource.org/licenses/gpl-2.0.php)

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The repository that contains the algorithms for generating domain names, dictionaries of malicious domain names. Developed to research the possibility of applying machine learning and neural networks to detect and classify malicious domains.

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  • Python 86.0%
  • C++ 12.2%
  • Perl 1.8%