  In this paper we introduce an intrusion detection system for Denial of
Service (DoS) attacks against Domain Name System (DNS). Our system architecture
consists of two most important parts: a statistical preprocessor and a neural
network classifier. The preprocessor extracts required statistical features in
a shorttime frame from traffic received by the target name server. We compared
three different neural networks for detecting and classifying different types
of DoS attacks. The proposed system is evaluated in a simulated network and
showed that the best performed neural network is a feed-forward backpropagation
with an accuracy of 99%.
