In this paper, the authors present an anomaly-based intrusion detection system to improve the system performance. Fuzzy rule-based modeling and fuzzy controller are used to create a detection model in the training phase and update this model in the test phase respectively. Moreover, the results of system's predictions buffered and presented to the system user later. After that, system user verifies these decisions and fuzzy controller tunes detection model using system user's feedbacks. They evaluated their system using the NCL dataset. Their dataset is a subset of KDD-99 dataset that does not contain any duplicated record. Furthermore, it includes a few difficult records that none of common classification methods in this area is able to classify them correctly.