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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15646

Title: Applying neural network to U2R attacks
Authors: Iftikhar Ahmad
Azween B. Abdullah
Abdullah S. Alghamdi
Keywords: U2R attack, Dataset, Multiple Layered Perceptron, Backpropagation, Detection Rate, Neural Network, False Positive, and False Negative,
Issue Date: 2010
Abstract: Intrusion detection using artificial neural networks is an ongoing area and thus interest in this field has increased among the researchers. Therefore, in this paper we present a system for tackling User to Root (U2R) attacks using generalized feedforward neural network. A backpropagation algorithm is used for training and testing purpose. The system uses sampled data from Kddcup99 dataset, an attack database that is a standard for evaluating the security detection mechanisms. The system is implemented in two phases such as training phase and testing phase. The developed system is applied to different U2R attacks to test its performance. Furthermore, the results indicate that this approach is more precise and accurate in case of false positive, false negative and detection rate.
URI: http://hdl.handle.net/123456789/15646
Appears in Collections:College of Computer and Information Sciences

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