The Application on Intrusion Detection Based on K-means Cluster Algorithm

Abstract

Internet security has been one of the most important problems in the world. Anomaly detection is the basic method to defend new attack in intrusion detection. Network intrusion detection is the process of monitoring the events occurring in a computing system or network and analyzing them for signs of intrusions, defined as attempts to compromise the confidentiality. A wide variety of data mining techniques have been applied to intrusion detections. In data mining, clustering is the most important unsupervised learning process used to find the structures or patterns in a collection of unlabeled data. We use the K-means algorithm to cluster and analyze the data in this paper. Computer simulations show that this method can detect unknown intrusions efficiently in the real network connections.