Modeling Intrusion Detection System by Discovering Association Rule in Rough Set Theory Framework

In Intrusion Detection Systems, many intelligent information processing methods, data miming technology and so on have been applied to generating attack signatures automatically, updating signatures easily and improving detection accuracy with ultra data set. This paper presents an improved association rule discovering system under rough set theory framework of modeling IDSs. The system makes association rule applicable in classifying fields. The system exploits data reductions, rule selection, feature selection to improve detection accuracy and reduce false alarm and unreal alarm. Empirical results illustrate that the intrusion detection model can detect intrusion accurately.