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In order to improve the accuracy of intrusion detection model, one mixed intrusion detection model was proposed in this paper, which combined with K-means algorithm, Naive Bayes algorithm and Back-Propagation neural network. In this work, as a partition-based, unsupervised cluster analysis method, K-means method was firstly applied. The data sets obtained were easily processed and learned by arbitrary machine learning algorithm in this form of clustering. Then, Bayes classifier processes these outcomes as a probability model. In this step, the fit and essential data attributes were achieved. Next, filter data samples learning was implemented by Back Propagation Neural Network, which was able to learn the patterns with less number of training cycles. Finally, the mixed intrusion detection model was validated by experiments on KDD CUP99’s datasets. Attacks as DoS, U2R, R2L and Probe were detected via the mixed intrusion detection model. The simulation experiments results show that the mixed intrusion detection model improved the accuracy and error rate compared with other models as well as the recall rate. Furthermore, this mixed intrusion detection model also demonstrates some value of practical application.