Generalized Discriminant Analysis Algorithm for Feature Reduction in Cyber Attack Detection System

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Executive Summary

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 the authors need to remove the worthless information from the original high dimensional database. To improve the generalization ability, they 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 they 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.

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