Research and Implementation of an Anomaly Detection Model Based on Clustering Analysis

Abstract

IDS (Intrusion Detection system) is an active and driving defense technology. This paper mainly focuses on intrusion detection based on data mining. The aim is to improve the detection rate and decrease the false alarm rate, and the main research method is clustering analysis. The algorithm and model of ID are proposed and corresponding simulation experiments are presented. Firstly, a method to reduce the noise and isolated points on the data set was advanced. By dividing and merging clusters and using the density radius of super sphere, an algorithm to calculate the number of the Cluster Centroid was given. By the more accurate method of finding k clustering center, an anomaly detection model was presented to get better detection effect. This paper used KDD CUP 1999 data set to test the performance of the model. The results show the system has a higher detection rate and a lower false alarm rate, it achieves expectant aim.