A Network Intrusion Detection Model Based on Principal Component Analysis and Random Forest

Abstract

Network intrusion data has the characteristics of high dimension, nonlinearity and redundancy. To solve the problem of low detection rate of traditional dimensionality reduction and detection methods, a network intrusion detection method based on principal component analysis combined with random forest is proposed. Firstly, the data dimension of network intrusion is reduced by principal component analysis to eliminate redundant information between data, and then the processed data is classified and trained by using random forest classifier. The algorithm is verified by the network intrusion NSL_KDD dataset. The experimental results show that compared with other network intrusion detection methods, the method has fast learning speed, high detection accuracy, low false negative rate and low false positive rate. An efficient, real-time and good network intrusion detection method.