Data mining based hybrid intrusion detection system

Abstract

An intrusion detection system is proposed using Decision Table/Naïve Bayes (DTNB). The Proposed system uses a hybrid classifier DTNB that is used to identify possible intrusions. The system is trained using a subset of the NSL KDD Cup dataset. The trained model is then tested using a subset of NSL KDD Cup dataset. The DTNB hybrid classifier is able to detect intrusion with a superior detection rate.