Aiming at the problems of insufficient feature extraction in the existing intrusion detection algorithms, the influence of feature weights was not considered, and the model classification was not accurate enough, an intrusion detection model based on the improved ReliefF algorithm was proposed. By optimizing the calculation of the feature weight of the intrusion data, an improved algorithm of ReliefF was proposed, based on the Pearson correlation coefficient of the calculated feature, a feature correlation scale was established. Only one of the features with high correlation was retained to realize the secondary optimization of the features, and finally decision tree, k-nearest neighbor, random forest, naive bayes and support vector machine classifier were used to evaluate the classification performance and accuracy. Experimental results on NSL-KDD and UNSW-NB15 data sets showed that this method could not only effectively reduce the feature dimension, but also had better detection performance, which had a positive effect on the computational complexity of the classifier.
