<div class="csl-bib-body">
<div class="csl-entry">Crnjanski, M. (2019). <i>Classifying encrypted network traffic based on deep learning</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.52261</div>
</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2019.52261
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/6557
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dc.description.abstract
An enormous IP traffic growth in the last decade has resulted in new requirements regarding network security. With the traffic growth, the cybersecurity is also changing.It is difficult to apply security measures because of the bigger traffic amount and new applications and services. A large percentage of network traffic, as well as network attacks, is encrypted, and it is important to recognize an attack quickly to prevent any damage to the running system. With traditional methods of traffic classification, such as the port-based traffic detection and deep packet inspection, it is very difficult to follow the demand of the modern traffic classification. In this thesis, machine learning is used as a solution to this problem. We developed a machine learning model based on binary classification which is able to detect attacks in encrypted network traffic. Our classification uses a new feature set, which consists of the following: the frame length, the time between packets in the flow and the direction of the flow. These are important features for us because their values do not change in encrypted traffic. The results open new discussions and change the view on today's traffic classification.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
traffic classification
en
dc.subject
deep learning
en
dc.subject
network security
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dc.title
Classifying encrypted network traffic based on deep learning
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2019.52261
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Milos Crnjanski
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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dc.contributor.assistant
Bachl, Maximilian
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tuw.publication.orgunit
E389 - Telecommunications
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC15520023
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dc.description.numberOfPages
80
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-131345
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.assistant.staffStatus
staff
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tuw.advisor.orcid
0000-0002-5391-467X
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
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item.languageiso639-1
en
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item.openaccessfulltext
Open Access
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item.openairetype
master thesis
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item.grantfulltext
open
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crisitem.author.dept
E389 - Telecommunications
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crisitem.author.parentorg
E350 - Fakultät für Elektrotechnik und Informationstechnik