Anomaly-based Network Intrusion Detection Methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86088633" target="_blank" >RIV/61989100:27240/13:86088633 - isvavai.cz</a>
Result on the web
<a href="http://advances.utc.sk/index.php/AEEE/article/view/877/909" target="_blank" >http://advances.utc.sk/index.php/AEEE/article/view/877/909</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Anomaly-based Network Intrusion Detection Methods
Original language 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 supportor 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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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