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Statements

Subject Item
n2:RIV%2F60460709%3A41110%2F04%3A5038%21RIV%2F2005%2FMSM%2F411105%2FN
rdf:type
skos:Concept n15:Vysledek
dcterms:description
Security of an information system is its very important property, especially today, when computers are interconnected via internet. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. For this purpose Intrusion Detection Systems (IDS) were designed. There are two basic models of IDS: misuse IDS and anomaly IDS. Misuse systems detect intrusions by looking for activity that corresponds to known signatures of intrusions or vulnerabilities. Anomaly systems detect intrusions by searching abnormal system activity. Most IDS commercial tools are misuse systems with rule-based expert system structure. However these techniques are less successful when attack characteristics vary from built in signatures. Artificial neural networks offer the potential to resolve these problems. As far as anomaly systems are concerned, it is very difficult to build them, because it is difficult to define normal and abnormal behaviour of a system. Also for building anomaly sys Security of an information system is its very important property, especially today, when computers are interconnected via internet. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. For this purpose Intrusion Detection Systems (IDS) were designed. There are two basic models of IDS: misuse IDS and anomaly IDS. Misuse systems detect intrusions by looking for activity that corresponds to known signatures of intrusions or vulnerabilities. Anomaly systems detect intrusions by searching abnormal system activity. Most IDS commercial tools are misuse systems with rule-based expert system structure. However these techniques are less successful when attack characteristics vary from built in signatures. Artificial neural networks offer the potential to resolve these problems. As far as anomaly systems are concerned, it is very difficult to build them, because it is difficult to define normal and abnormal behaviour of a system. Also for building anomaly sys Security of an information system is its very important property, especially today, when computers are interconnected via internet. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. For this purpose Intrusion Detection Systems (IDS) were designed. There are two basic models of IDS: misuse IDS and anomaly IDS. Misuse systems detect intrusions by looking for activity that corresponds to known signatures of intrusions or vulnerabilities. Anomaly systems detect intrusions by searching abnormal system activity. Most IDS commercial tools are misuse systems with rule-based expert system structure. However these techniques are less successful when attack characteristics vary from built in signatures. Artificial neural networks offer the potential to resolve these problems. As far as anomaly systems are concerned, it is very difficult to build them, because it is difficult to define normal and abnormal behaviour of a system. Also for building anomaly sys
dcterms:title
Neural networks in intrusion detection systems Neural networks in intrusion detection systems Neuronové sítě v systémech pro detekci napadení
skos:prefLabel
Neural networks in intrusion detection systems Neuronové sítě v systémech pro detekci napadení Neural networks in intrusion detection systems
skos:notation
RIV/60460709:41110/04:5038!RIV/2005/MSM/411105/N
n5:strany
35;39
n5:aktivita
n13:Z
n5:aktivity
Z(MSM 411100010)
n5:cisloPeriodika
1
n5:dodaniDat
n8:2005
n5:domaciTvurceVysledku
n11:2720949 n11:8344051
n5:druhVysledku
n6:J
n5:duvernostUdaju
n10:S
n5:entitaPredkladatele
n12:predkladatel
n5:idSjednocenehoVysledku
575938
n5:idVysledku
RIV/60460709:41110/04:5038
n5:jazykVysledku
n16:eng
n5:klicovaSlova
intrusion detection system (IDS), misuse IDS, anomaly IDS, Kohonen´s self-organizing maps
n5:klicoveSlovo
n14:misuse%20IDS n14:anomaly%20IDS n14:Kohonen%C2%B4s%20self-organizing%20maps n14:intrusion%20detection%20system%20%28IDS%29
n5:kodStatuVydavatele
CZ - Česká republika
n5:kontrolniKodProRIV
[49E1FC8A11CF]
n5:nazevZdroje
Zemědělská ekonomika
n5:obor
n9:JD
n5:pocetDomacichTvurcuVysledku
2
n5:pocetTvurcuVysledku
2
n5:rokUplatneniVysledku
n8:2004
n5:svazekPeriodika
50
n5:tvurceVysledku
Brechlerová, Dagmar Veselý, Arnošt
n5:zamer
n17:MSM%20411100010
s:issn
0139-570X
s:numberOfPages
5
n18:organizacniJednotka
41110