On the combination of naive Bayes and decision trees for intrusion detection

Decision trees and naive Bayes have been recently used as classifiers for intrusion detection problems. They present good complementarities in detecting different kinds of attacks. However, both of them generate a high number of false negatives. This paper proposes a hybrid classifier that exploits complementaries between decision trees and naive Bayes. In order to reduce false negative rate, we propose to reexamine decision trees and Bayes nets outputs by an anomaly-based detection system