In order to enhance the accuracy and generalization of the detection model,an intrusion detection model of random attention capsule network with variable fusion was proposed.Through dynamic feature fusion,the model could better capture data features.At the same time,random attention mechanism was used to reduce the dependence on training data and make the model more generalization.The model was validated on NSL-KDD and UNSW-NB15 datasets.The experimental results show that the accuracy of the model on the two test sets is 99.49% and 98.60% respectively
