Performance Evaluation of PCA Filter In Clustered Based Intrusion Detection System

The study, analysis and exploration of recent development of data mining applications such as classification and clustering is one of the needs for machine learning algorithms to be applied to large scale data will lead to acquire the direction of future research. It would be future demand in IDS for detecting the intrusions in mobile network. This paper presents the comparison of different clustering techniques. Also focus on the effect of Principal Component Analysis filter on these clustered based methods.The aim of this paper is to investigate the performance of different clustering methods for a set of large data. The algorithms are tested on intrusion detection data set. A fundamental review on the selected clustering techniques is presented for introduction purposes. The KDD data set is used for this purpose. Subsequently, clustering technique that has the potential to significantly improve the conventional methods will be suggested for the use in intrusion detection in mobile network data.