A Mixed Unsupervised Clustering-Based Intrusion Detection Model

Abstract

Through analyzing the advantages and disadvantages between anomaly detection and misuse detection, a mixed intrusion detection system (IDS) model is designed. First, data is examined by the misuse detection module, then abnormal data detection is examined by anomaly detection module. In this model, the anomaly detection module is built using unsupervised clustering method, and the algorithm is an improved algorithm of K-means clustering algorithm and it is proved to have high detection rate in the anomaly detection module.