Intrusion Detection Model Based on Support Vector Regression and Principal Components Analysis

Abstract

To overcome the deficiencies of low accuracy and high false alarm rate in network intrusion detection system, an integrated Intrusion detection model based on support vector regression (SVR) and principal components analysis (PCA) is proposed in the paper. Utilizing the character that PCA algorithm can keep the discernability of original dataset after reduction, the reduces of the original dataset are calculated and used to train individual SVR classifier for ensemble, which increase the diversity between individual classifiers, and consequently, increase the detection accuracy. To validate the effectiveness of the proposed method, simulation experiments are performed based on the KDD 99 dataset. The results show that the proposed method is a promised ensemble method owning to its high diversity, high detection accuracy and faster speed in intrusion detection.