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Vulnerabilities in common security components such as firewalls are inevitable. Intrusion Detection Systems (IDS) are used as another wall to protect computer systems and to identify corresponding vulnerabilities. This paper presents a new ensemble-based method for intrusion detection. This method uses feature transformation to create the needed diversity between base classifiers. In other words, first different sets of features are created by mapping the original features into new spaces where the samples are well separated, and then each base classifier is trained on one of these newly created features sets. The proposed method for constructing an ensemble of classifiers is a general method which may be used in any classification problem. KDD-99 dataset is used to evaluate the proposed method and the results are compared with some recent works in the literature using the same dataset. The results of comparing the performance of the proposed method with other alternative classification methods are encouraging.