Automatically Tuning Intrusion Detection System (ATIDS) makes tuning automatically. The key idea is to use the binary SLIPPER as a basic module, which is a rule learner based on confidence-rated boosting. This system is evaluated using the NSL KDD intrusion detection dataset. An experimental result shows the SCIDS system with SLIPPER algorithm gives better performance in terms of detection rate, false alarm rate, total misclassification cost and cost per example on NSL-KDD dataset than that of on KDD.