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