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This Generalized Discriminant Analysis (GDA) has provided an extremelypowerful approach to extracting non linear features. The network traffic dataprovided for the design of intrusion detection system always are large withineffective information, thus we need to remove the worthless information fromthe original high dimensional database. To improve the generalization ability,we usually generate a small set of features from the original input variablesby feature extraction. The conventional Linear Discriminant Analysis (LDA)feature reduction technique has its limitations. It is not suitable for nonlinear dataset. Thus we propose an efficient algorithm based on the GeneralizedDiscriminant Analysis (GDA) feature reduction technique which is novel approachused in the area of cyber attack detection. This not only reduces the number ofthe input features but also increases the classification accuracy and reducesthe training and testing time of the classifiers by selecting mostdiscriminating features. We use Artificial Neural Network (ANN) and C4.5classifiers to compare the performance of the proposed technique. The resultindicates the superiority of algorithm.

Generalized Discriminant Analysis algorithm for feature reduction in Cyber Attack Detection System