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Worms are on the top of malware threats attacking computer system although of the evolution of worm detection techniques. Early detection of unknown worms is still a problem. This paper produce a method for detecting unknown worms based on local victim information. The proposed system uses Artificial Neural Network (ANN) for classifying worm/non-worm traffic and predicting the percentage of infection in the infected network. This prediction can be used to support decision making process for network administrator to respond quickly to worm propagation in an accurate procedure.
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