In order to better solve the problem of increasing the false positive rate of unknown intrusion behaviors by misuse detection in intrusion detection technology, an intrusion detection technology based on NBSR model is proposed. Firstly, in order to compensate for the lack of correlation analysis between features by the ReliefF feature selection algorithm, the Pearson correlation coefficient is introduced and the Relieff-P algorithm is proposed. Secondly, the Relieff-P algorithm is used to process the UNSW-NB15 dataset to remove irrelevant features and obtain a new feature subset. Finally, the naive Bayes classifier and the Softmax regression classifier are cascaded to form the NBSR classifier, and NBSR model was established. The experimental results on the UNSW-NB15 test set show that the NBSR model has lower false positive rate than other detection models.