Network Intrusion Detection Method Based on PCA and Bayes Algorithm

Abstract

Intrusion detection refers to monitoring network data information, quickly detecting intrusion behavior, can avoid the harm caused by intrusion to a certain extent. Traditional intrusion detection methods are mainly focused on rule files and data mining. They have the disadvantage of not being able to detect new types of attacks and have the slow detection speed. To address these issues, an intrusion detection method based on improved PCA combined with Gaussian Naive Bayes was proposed. By weighting the first few feature vectors of the traditional PCA, data pollution can be reduced. The number of final weighted principal components is 2 through sequential selection. The dimensionality reduction of the data is achieved through improved PCA. Finally, the intrusion behaviors were detected by using the Gaussian Naive Bayes classifier. The indexes of detection accuracy, detection time, precision rate, and recall rate were applied to evaluate the results. The experimental results show that, comparing with the traditional Bayes method, the method proposed in this article can reduce the detection time by 60%, shorten it to 0.5s, and increase the detection rate to 91.06%. The mean value of detection accuracy is about 86% by cross-validation.