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In this paper the authors introduce an intrusion detection system for Denial of Service (DoS) attacks against Domain Name System (DNS). The system architecture consists of two most important parts: a statistical preprocessor and a neural network classifier. The preprocessor extracts required statistical features in a short-time frame from traffic received by the target name server. The authors 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 back-propagation with an accuracy of 99%.
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