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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<jats:italic> 2<\/jats:italic> 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. 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