Anomaly-based Network Intrusion Detection Methods

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Anomaly-based Network Intrusion Detection Methods

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Anomaly-based Network Intrusion Detection Methods

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Advances in Electrical and Electronic Engineering

ISSN

1336-1376

e-ISSN

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Svazek periodika

Vol 11

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

7

Strana od-do

468 - 474

Kód UT WoS článku

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EID výsledku v databázi Scopus

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