With the growth of networked computers and associated applications, intrusion detection has become essential to keeping networks secure. A number of intrusion detection methods have been developed for protecting computers and networks using conventional statistical methods as well as data mining methods. It is necessary that the capabilities of intrusion detection methods be updated with the creation of new attacks. This paper proposes a hybrid intrusion detection method that uses a combination of supervised and outlier based methods for improving the efficiency of detection of new and old attacks. The method is evaluated with the benchmark intrusion dataset called the knowledge discovery and data mining Cup 1999 dataset and the new version of KDD (NSL-KDD) dataset. Thus the performance of our method is very good.