The sharp increase in the number of smartphones makes the fake Apps in Android platform to be an urgent issue. Many approaches have been proposed to defend against these fake Apps. However, most of them have some disadvantage, such as low accuracy, not general, or not robust in big data. This paper proposes an automatic fake Apps detection method by using Decision Tree Ensembles based Detection (i.e., DTED). The DTED is a three-level ensemble method which capitalizes different voting technology. We extract permission features and API calls features relevant to fake Apps behavior by analyzing a large number of samples. Experimental results show that our method achieves accuracy as high as 91.6% and 91.13% F-measure, which performs better than other algorithms in big data.