A hybrid intelligent approach for network intrusion detection
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86092952" target="_blank" >RIV/61989100:27240/12:86092952 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1016/j.proeng.2012.01.827" target="_blank" >http://dx.doi.org/10.1016/j.proeng.2012.01.827</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.proeng.2012.01.827" target="_blank" >10.1016/j.proeng.2012.01.827</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A hybrid intelligent approach for network intrusion detection
Popis výsledku v původním jazyce
Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behaviour or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper, we propose to use a hybrid intelligent approach using combination of classifiers in order to make the decision intelligently, so that the overall performance of the resultant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or clusterer first on the whole training dataset and then the output is applied to another classifier to classify the data. We use 2-class classification strategy along with 10-fold cross validation method to produce the final classification results in terms of normal or intrusion. Experimental results on NSL-KDD dataset, an improved version of KDDCup 1999 dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.
Název v anglickém jazyce
A hybrid intelligent approach for network intrusion detection
Popis výsledku anglicky
Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behaviour or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper, we propose to use a hybrid intelligent approach using combination of classifiers in order to make the decision intelligently, so that the overall performance of the resultant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or clusterer first on the whole training dataset and then the output is applied to another classifier to classify the data. We use 2-class classification strategy along with 10-fold cross validation method to produce the final classification results in terms of normal or intrusion. Experimental results on NSL-KDD dataset, an improved version of KDDCup 1999 dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
IN - Informatika
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2012
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Procedia Engineering. Volume 30
ISBN
—
ISSN
1877-7058
e-ISSN
—
Počet stran výsledku
9
Strana od-do
1-9
Název nakladatele
Elsevier
Místo vydání
Amsterdam
Místo konání akce
Coimbatore
Datum konání akce
7. 12. 2011
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000314170600001