Intrusion Detection System (IDS) has become an essential part of network security system, the practicability and durability of the existing intrusion detection methods still have improvement space, like detecting intrusion threats earlier and improving the detection accuracy of intrusion detection systems. Therefore, an intrusion detection method based on Ensemble Transfer Learning (ETL) via weighted mutual information was proposed. Firstly, the transfer strategy was used to model multiple feature sets, then the mutual information was used to measure the data attribution of feature sets under the transfer models in different domains. Finally, the weighted ensemble was performed to the multiple transfer models according to the measures, obtaining the ensemble transfer model. The method was able to construct the intrusion detection model better than the traditional models without ensemble or transfer learning by learning the knowledge of little labeled samples in the new environment and many labeled samples in the prior environment. The benchmark NSL-KDD dataset was used to evaluate the proposed method and the results show that the proposed method has good convergence performance and improve the accuracy of intrusion detection.