Detecting DGA Malware traffic through Behavioral Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00306875" target="_blank" >RIV/68407700:21230/16:00306875 - isvavai.cz</a>

Result on the web

<a href="http://ieeexplore.ieee.org/document/7585238/" target="_blank" >http://ieeexplore.ieee.org/document/7585238/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ARGENCON.2016.7585238" target="_blank" >10.1109/ARGENCON.2016.7585238</a>

Result language

angličtina

Original language name

Detecting DGA Malware traffic through Behavioral Models

Original language description

Abstract: Some botnets use special algorithms to generate the domain names they need to connect to their command and control servers. They are refereed as Domain Generation Algorithms. Domain Generation Algorithms generate domain names and tries to resolve their IP addresses. If the domain has an IP address, it is used to connect to that command and control server. Otherwise, the DGA generates a new domain and keeps trying to connect. In both cases it is possible to capture and analyze the special behavior shown by those DNS packets in the network. The behavior of Domain Generation Algorithms is difficult to automatically detect because each domain is usually randomly generated and therefore unpredictable. Hence, it is challenging to separate the DNS traffic generated by malware from the DNS traffic generated by normal computers. In this work we analyze the use of behavioral detection approaches based on Markov Models to differentiate Domain Generation Algorithms traffic from normal DNS traffic. The evaluation methodology of our detection models has focused on a real-time approach based on the use of time windows for reporting the alerts. All the detection models have shown a clear differentiation between normal and malicious DNS traffic and most have also shown a good detection rate. We believe this work is a further step in using behavioral models for network detection and we hope to facilitate the development of more general and better behavioral detection methods of malware traffic.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

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Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

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

Article name in the collection

2016 IEEE Biennial Congress of Argentina

ISBN

978-1-4673-9764-3

ISSN

—

e-ISSN

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Number of pages

6

Pages from-to

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Publisher name

American Institute of Physics and Magnetic Society of the IEEE

Place of publication

San Francisco

Event location

Buenos Aires

Event date

Jun 15, 2016

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000386665200002