  In this paper, we introduce new learning algorithms for reducing false
positives in intrusion detection. It is based on decision tree-based attribute
weighting with adaptive na\"ive Bayesian tree, which not only reduce the false
positives (FP) at acceptable level, but also scale up the detection rates (DR)
for different types of network intrusions. Due to the tremendous growth of
network-based services, intrusion detection has emerged as an important
technique for network security. Recently data mining algorithms are applied on
network-based traffic data and host-based program behaviors to detect
intrusions or misuse patterns, but there exist some issues in current intrusion
detection algorithms such as unbalanced detection rates, large numbers of false
positives, and redundant attributes that will lead to the complexity of
detection model and degradation of detection accuracy. The purpose of this
study is to identify important input attributes for building an intrusion
detection system (IDS) that is computationally efficient and effective.
Experimental results performed using the KDD99 benchmark network intrusion
detection dataset indicate that the proposed approach can significantly reduce
the number and percentage of false positives and scale up the balance detection
rates for different types of network intrusions.
