Intrusion detection requires the system to identify network intrusions quickly and accurately, so it also requires high efficiency of the detection algorithm. In order to improve the efficiency and accuracy of intrusion detection system, and reduce the rate of false positives and false negatives, a H-C4.5-NB intrusion detection model combined C4.5 with Naive Bayes (NB) was proposed after fully analyzing the C4.5 and NB algorithm. The distribution of decision category was described in the form of probability in this model, and the final decision results were given in the form of C4.5 and NB probability weighted sum. Finally the performance of the model was tested by KDD 99 data set. The experimental results showed that the accuracy of Denial of Service (DoS) was improved about 9% and the accuracy of U2R and R2L was improved about 20%-30% in H-C4.5-NB compared to the traditional methods such as C4.5, NB and NBTree.