The aim of this paper is to explore the effectiveness of Bayesian classifiers in intrusion detection (ID). Specifically, we provide an experimental study that focuses on comparing the accuracy of different classification models showing that the Bayesian classification approach is reasonably effective and efficient in predicting attacks and in exploiting the knowledge required by a computational intelligent ID process.
Intelligent Bayesian Classifiers in Network Intrusion Detection
BOSIN, ANDREA;DESSI, NICOLETTA;PES, BARBARA
2005-01-01
Abstract
The aim of this paper is to explore the effectiveness of Bayesian classifiers in intrusion detection (ID). Specifically, we provide an experimental study that focuses on comparing the accuracy of different classification models showing that the Bayesian classification approach is reasonably effective and efficient in predicting attacks and in exploiting the knowledge required by a computational intelligent ID process.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
