This HTML5 document contains 45 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n5http://localhost/temp/predkladatel/
n12http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n9http://linked.opendata.cz/resource/domain/vavai/subjekt/
n8http://linked.opendata.cz/ontology/domain/vavai/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
rdfshttp://www.w3.org/2000/01/rdf-schema#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n16http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F61989100%3A27240%2F13%3A86088633%21RIV14-MSM-27240___/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n17http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n18http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n19http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n14http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n6http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F13%3A86088633%21RIV14-MSM-27240___
rdf:type
skos:Concept n8:Vysledek
rdfs:seeAlso
http://advances.utc.sk/index.php/AEEE/article/view/877/909
dcterms:description
The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization our results. The WEKA is a collection of machine learning algorithms for data mining tasks. The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization our results. The WEKA is a collection of machine learning algorithms for data mining tasks.
dcterms:title
Anomaly-based Network Intrusion Detection Methods Anomaly-based Network Intrusion Detection Methods
skos:prefLabel
Anomaly-based Network Intrusion Detection Methods Anomaly-based Network Intrusion Detection Methods
skos:notation
RIV/61989100:27240/13:86088633!RIV14-MSM-27240___
n8:predkladatel
n9:orjk%3A27240
n3:aktivita
n7:V
n3:aktivity
V
n3:cisloPeriodika
6
n3:dodaniDat
n6:2014
n3:domaciTvurceVysledku
n12:3589293 n12:2573873 n12:3570223 n12:7055943
n3:druhVysledku
n19:J
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
61318
n3:idVysledku
RIV/61989100:27240/13:86088633
n3:jazykVysledku
n18:eng
n3:klicovaSlova
Anomaly-based detection, attack, bayesian networks, WEKA
n3:klicoveSlovo
n17:attack n17:WEKA n17:Anomaly-based%20detection n17:bayesian%20networks
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[D367EF45D4B2]
n3:nazevZdroje
Advances in Electrical and Electronic Engineering
n3:obor
n14:JC
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n6:2013
n3:svazekPeriodika
Vol 11
n3:tvurceVysledku
Nevlud, Pavel Kapičák, Lukáš Bureš, Miroslav Zdrálek, Jaroslav
s:issn
1336-1376
s:numberOfPages
7
n5:organizacniJednotka
27240