With the development of the Internet, network security has become an indispensable factor of computer technology. Intrusion Detection Systems (IDS) play an important role in network security. One aspect which affects the accuracy and performance of IDS are classifiers. This paper proposes a new approach which combines different classifiers in order to make best use of each classifier. To build the new model, the authors evaluate the accuracy and performance (training and testing time) of three classification algorithms: ID3, Naitive Bayes and SVM. The experimental results using the KDDCup'99 IDS dataset based on the 10-fold cross validation test shows that against anyone particular type of attack, one of the classifiers functions best. The purpose of this study is to enhance the accuracy and performance of IDS against particular types of attacks.