  In this paper, a new learning algorithm for adaptive network intrusion
detection using naive Bayesian classifier and decision tree is presented, which
performs balance detections and keeps false positives at acceptable level for
different types of network attacks, and eliminates redundant attributes as well
as contradictory examples from training data that make the detection model
complex. The proposed algorithm also addresses some difficulties of data mining
such as handling continuous attribute, dealing with missing attribute values,
and reducing noise in training data. Due to the large volumes of security audit
data as well as the complex and dynamic properties of intrusion behaviours,
several data miningbased intrusion detection techniques have been applied to
network-based traffic data and host-based data in the last decades. However,
there remain various issues needed to be examined towards current intrusion
detection systems (IDS). We tested the performance of our proposed algorithm
with existing learning algorithms by employing on the KDD99 benchmark intrusion
detection dataset. The experimental results prove that the proposed algorithm
achieved high detection rates (DR) and significant reduce false positives (FP)
for different types of network intrusions using limited computational
resources.
