Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
Decision tree based light weight intrusion detection using a wrapper approach
Expert Systems with Applications: An International Journal
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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 complentaries between decision trees and naive bayes. In order to reduce false negative rate, we propose to reexaminate decision trees and Bayes nets outputs by an anomalybased detection system.