An anomaly detection algorithm based on clustering

At present analyzing mass data in network by data mining technology in order to detect intrusion has become focus of anomaly detection research. In order to improve quality of intrusion detection, an improved anomaly detection algorithm is proposed in this paper. Firstly the training data set is converted to the standard unit features metric space, then the improved algorithm is used to divide the data in order to find the clustering center. In end of this paper the improved algorithm is analyzed and compared with old algorithm. Experimental results show that the improved algorithm has good stability and can detect intrusions in real network data effectively. It has better scalability on large data set.