A Clustering Algorithm for Intrusion Detection using Hybrid Data Mining Technique

Abstract: Intrusion Detection System (IDS) is a becoming a necessary component of any network in today's world of Internet. It is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. The main reason for using data mining classification method for Intrusion Detection System is due to the enormous volume of existing and newly appearing network data that require processing. Data mining is the best option for handling such type of data. This paper focuses on a hybrid approach for intrusion detection system (IDS) based on data mining techniques. Clustering analysis is required to improve the detection rate and decrease the false alarm rate. Most of the previously proposed methods suffer from the low detection rate and high false alarm rate. This paper uses hybrid data mining approach that contains feature selection, filtering, clustering, divide and merge and clustering ensemble. The IDS with clustering ensemble is introduced for the effective identification of attacks to achieve high accuracy and detection rate as well as low false alarm rate. Keywords: Intrusion detection system;datamining;false alaram rate; KDD CUP99 data set;detection rate.