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Using neural networks in intrusion detection systems

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dc.title Using neural networks in intrusion detection systems en Merhaut, Filip Zelinka, Ivan
dc.relation.ispartof MENDEL 2008
dc.identifier.isbn 978-80-214-3675-6 2008
dc.citation.spage 172
dc.citation.epage 174
dc.event.title 14th International Conference on Soft Computing
dc.event.location Brno
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2008-06-18
dc.event.edate 2008-06-20
dc.type conferenceObject
dc.language.iso en
dc.publisher Vysoké učení technické v Brně cs
dc.subject Intrusion Detection System en
dc.subject computer security en
dc.subject artificial neural networks en
dc.subject open source en
dc.subject snort en
dc.description.abstract This paper sets out the possibility of deployment of neural networks in the most widespread open source IDS program Snort. The principle is to capture packets of network traffic and then to use neural network to increase the likelihood of identifying attack in the stream by dynamically identifying the operating systems on the individual protected hosts. Thanks to the use of neural network this system should be able to overcome some of the evasive techniques the attackers use. en
utb.faculty Faculty of Applied Informatics
utb.identifier.rivid RIV/70883521:28140/08:63507092!RIV09-GA0-28140___
utb.identifier.obdid 43857095
utb.identifier.scopus 2-s2.0-84898865143
utb.identifier.wok 000265681300030
utb.source d-wok 2011-08-09T07:34:07Z 2011-08-09T07:34:07Z
utb.contributor.internalauthor Merhaut, Filip
utb.contributor.internalauthor Zelinka, Ivan
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