A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Applying data mining to intrusion detection: the quest for automation, efficiency, and credibility
ACM SIGKDD Explorations Newsletter
Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
Hi-index | 0.00 |
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.