Computer and network systems now-a-days are facing many security issues, one of which considered important is intrusion. To prevent such intrusion, a mechanism for optimal intrusion detection is deemed necessary. A number of tools and techniques are available, yet most of them still face a main problem that is on performance. The performance, in essence, can be increased by reducing false positives and increasing accurate detection rate. What has made the performance terrible in the existing intrusion detection approaches is due to the use of a raw dataset that includes redundancy and leads the classifier to be confused.