  The main function of IDS (Intrusion Detection System) is to protect the
system, analyze and predict the behaviors of users. Then these behaviors will
be considered an attack or a normal behavior. Though IDS has been developed for
many years, the large number of return alert messages makes managers maintain
system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM
(Support Vector Machine) to detect intrusions. First, RST is used to preprocess
the data and reduce the dimensions. Next, the features were selected by RST
will be sent to SVM model to learn and test respectively. The method is
effective to decrease the space density of data. The experiments will compare
the results with different methods and show RST and SVM schema could improve
the false positive rate and accuracy.
