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Distributed Denial of Service (DDoS) attacks are serious threats for availability of the internet services. These types of attacks command multiple agents to send a great number of packets to a victim and thus can easily exhaust the resources of the victim. In this paper, the authors propose an anomaly-based DDoS detection method based on the various features of attack packets, obtained from study the incoming network traffic and using of Radial Basis Function (RBF) neural networks to analyze these features.
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