Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection

Abstract

Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) feature extraction algorithms is implemented. This ensemble PCA-LDA method has led to good results and showed a greater proportion of precision in comparison to a single feature extraction method.